[mcp-analysis] GitHub MCP Structural Analysis - 2026-02-06

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This analysis evaluates GitHub MCP tool responses for size (tokens), structure (schema design), and usefulness for agentic workflows. Testing covered 14 representative tools across 6 toolsets with minimal parameters to assess response efficiency and actionability.

Key Findings: Most GitHub MCP tools are well-optimized for agents, with 11 of 14 tools rated 4+ for usefulness. The standout tools balance low token usage with complete, actionable data. Context-heavy tools (2000+ tokens) are detailed but may benefit from optional field filtering.

Executive Summary

Metric	Value
Tools Analyzed	14
Total Tokens (Today)	12,800
Average Tokens per Tool	914
Average Usefulness Rating	4.71/5
Best Rated Tools	get_file_contents, list_branches, list_tags, list_commits (5/5)
Lowest Token Usage	get_file_contents (25 tokens)
Highest Token Usage	list_workflows (3,200 tokens)

Visualizations

Response Size by Toolset

Analysis: Actions and pull_requests toolsets have the highest average token usage (2900-3200), while repos toolset is most efficient (175 avg). Search toolset shows moderate usage (617 avg), balancing comprehensiveness with efficiency.

Usefulness Ratings

Analysis: 11 of 14 tools (79%) rated 4 or above. Only 3 tools rated exactly 4.0, with none below. This indicates strong overall quality for agentic work.

Token Size vs Usefulness

Analysis: Ideal Zone (low tokens, high rating): get_file_contents, list_branches, list_tags, search_repositories, list_discussions, get_commit, list_commits. These tools provide excellent value with minimal context usage.

Context-Heavy Zone (high tokens, high rating): list_workflows, list_pull_requests, pull_request_read. Detailed but expensive. Consider lazy-loading nested objects.

Schema Complexity

Analysis: Nesting depth ranges from 1 (get_file_contents) to 5 (list_pull_requests, pull_request_read). Shallow nesting (≤2) is ideal for parsing. Deep nesting (4-5) requires careful traversal but includes rich relationships.

Full Structural Analysis Report

Usefulness Ratings for Agentic Work

Tool	Toolset	Rating	Token Size	Assessment
get_file_contents	repos	⭐⭐⭐⭐⭐	25	Excellent - Minimal overhead, just file contents. Perfect efficiency.
list_branches	repos	⭐⭐⭐⭐⭐	100	Excellent - Clean, minimal response with essential data.
list_tags	repos	⭐⭐⭐⭐⭐	150	Excellent - Efficient tag list with commit SHA and URLs.
search_repositories	search	⭐⭐⭐⭐⭐	250	Excellent - Complete repo metadata, stars, topics. Immediately actionable.
get_commit	repos	⭐⭐⭐⭐⭐	275	Excellent - Option to exclude diff saves tokens. Smart design.
list_discussions	discussions	⭐⭐⭐⭐⭐	275	Excellent - GraphQL pagination with category and user info.
list_commits	repos	⭐⭐⭐⭐⭐	325	Excellent - Clean commit history with author details.
issue_read	issues	⭐⭐⭐⭐⭐	550	Excellent - Complete issue details, reactions, labels. Good balance.
list_issues	issues	⭐⭐⭐⭐⭐	650	Excellent - GraphQL response with full pagination support.
search_issues	search	⭐⭐⭐⭐⭐	650	Excellent - Clean search with all needed metadata.
search_code	search	⭐⭐⭐⭐	950	Good - Includes full repo context. Some redundancy but useful.
pull_request_read	pull_requests	⭐⭐⭐⭐	2,600	Good - Very detailed with stats. Context-heavy due to nested repos.
list_pull_requests	pull_requests	⭐⭐⭐⭐	2,800	Good - Comprehensive PR data. Redundant repo metadata in head/base.
list_workflows	actions	⭐⭐⭐⭐	3,200	Good - Complete workflow list. Large responses when many workflows.

Schema Analysis

Tool	Type	Depth	Pagination	Key Characteristics
get_file_contents	string	1	N/A	Raw text response. No wrapper overhead.
list_branches	array	2	REST	Simple array of objects with name, sha, protected flag.
list_tags	array	2	REST	Array with name, commit SHA, download URLs.
list_workflows	object	2	REST	Total count + workflows array. Flat structure, many items.
search_repositories	object	3	REST	Total count + items array with repo metadata.
get_commit	object	3	N/A	Nested commit object with author/committer details.
list_discussions	object	3	GraphQL cursor	Discussions array + pageInfo with cursor pagination.
list_commits	array	3	REST	Array of commit objects with nested commit details.
issue_read	object	3	N/A	Single issue with labels, user, reactions.
list_issues	object	4	GraphQL cursor	Issues array + pageInfo + totalCount. Rich structure.
search_issues	object	4	REST	Total count + items with full issue metadata.
search_code	object	4	REST	Items array with nested repository objects per result.
list_pull_requests	array	5	REST	Deep nesting with full repo objects in head/base branches.
pull_request_read	object	5	N/A	Complete PR with nested repo objects, assignees, reviewers.

Response Size Analysis

Toolset	Avg Tokens	Min	Max	Tools Tested
actions	3,200	3,200	3,200	1
pull_requests	2,700	2,600	2,800	2
search	617	250	950	3
issues	600	550	650	2
discussions	275	275	275	1
repos	175	25	325	5

Key Observations

Pagination Patterns:

• GraphQL Cursor: Used by list_issues, list_discussions. Provides cursor-based pagination with pageInfo (hasNextPage, endCursor). Excellent for large datasets.
• REST Offset: Used by most tools. Simple page/perPage parameters. Works well for smaller datasets.

Data Redundancy:

• Pull Request Tools: Include full repository objects in both head and base branches, duplicating repo metadata (topics, license, stats). Adds ~800 tokens per PR.
• Search Tools: Include repository context in each search result item. Useful for cross-repo searches but adds overhead.

Schema Consistency:

• Most tools follow consistent patterns: objects with arrays, standard user objects, consistent field naming.
• GraphQL tools (list_issues, list_discussions) have unified structure with pageInfo.

Context Efficiency Wins:

• get_file_contents: Returns raw text, no JSON overhead.
• get_commit: Optional include_diff parameter saves significant tokens when only metadata needed.
• list_branches, list_tags: Minimal fields, no unnecessary nesting.

Recommendations

For Agentic Workflows:

1. Prefer These Tools (Rating 5/5, Low Tokens):

   • get_file_contents - Most efficient
   • list_branches - Clean branch info
   • list_tags - Efficient release tracking
   • search_repositories - Great for repo discovery
   • list_commits - Good commit history

2. Use Carefully (Rating 4/5, High Tokens):

   • list_workflows - Only when needed, can be very large
   • list_pull_requests - Consider filtering by state/head
   • pull_request_read - Use sparingly, get specific PRs only

3. Optimization Opportunities:

   • Add field filtering to list_pull_requests and pull_request_read to exclude nested repo objects when not needed
   • Consider sparse fieldsets for list_workflows when only names/IDs needed
   • Document include_diff=false pattern for other tools that could benefit

4. Best Practices:

   • Always use perPage=1 or small values for discovery queries
   • Use include_diff=false on get_commit when diff not needed
   • Prefer GraphQL tools (list_issues, list_discussions) for large paginated datasets
   • Cache frequently accessed data (branches, tags, repo metadata)

Tool Selection Guide

For code exploration:

• 🥇 search_code - Find specific code patterns
• 🥈 get_file_contents - Read specific files
• 🥉 list_commits - Understand history

For issue tracking:

• 🥇 search_issues - Find specific issues across repos
• 🥈 list_issues - List issues with pagination
• 🥉 issue_read - Get full issue details

For PR workflows:

• 🥇 list_pull_requests - Discover PRs (use filters)
• 🥈 pull_request_read - Get specific PR details
• 🥉 list_commits - Review PR commit history

**For repo meta(redacted)

• 🥇 search_repositories - Find repos
• 🥈 list_branches - Check branches
• 🥉 list_tags - Track releases

Methodology

Testing Approach:

• Called each tool with minimal parameters (perPage=1 where applicable)
• Estimated token counts (1 token ≈ 4 characters)
• Analyzed JSON schema: type, nesting depth, key fields
• Rated usefulness for autonomous agents (1-5 scale)

Rating Criteria:

• 5: Complete, actionable data with clear structure and minimal overhead
• 4: Most needed data present, minor gaps or optimization opportunities
• 3: Usable but requires additional calls or complex parsing
• 2: Missing key data or confusing structure
• 1: Minimal value for agentic tasks

References:

• §21748531745

AI generated by GitHub MCP Structural Analysis

• expires on Feb 13, 2026, 11:18 AM UTC

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This discussion was automatically closed because it expired on 2026-02-13T11:18:40.790Z.

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