The github-codemunch-mcp server enables structured, token-efficient code retrieval for AI agents by indexing GitHub repositories and providing precise symbol-level access — reducing token usage by 95%+ compared to reading entire files.
Core Capabilities:
Index repositories (index_repo): Fetch, parse, and extract symbols from a GitHub repo into local storage, with optional AI-generated summaries per symbol.
List indexed repositories (list_repos): View all repositories available for querying.
Browse file tree (get_file_tree): Retrieve the directory structure of an indexed repo, with optional path filtering.
Get file outline (get_file_outline): List all symbols in a file (functions, classes, methods, etc.) with signatures and summaries — without loading full file content.
Search symbols (search_symbols): Search by name, signature, summary, or docstring across a repo, with filters for symbol kind (function, class, method, constant, type) and file glob patterns.
Retrieve symbol source (get_symbol / get_symbols): Fetch exact source code for one or multiple symbols by ID, avoiding full file reads.
Advanced Features:
Structural code analysis: detect dead code, identify symbol importers, assess blast radius of changes, traverse class hierarchies, and map Git diffs to symbols.
Architectural layer enforcement: define and validate dependency rules across codebase layers.
Token optimization controls: disable unused tools, filter languages, and tune description verbosity.
Multi-language support via tree-sitter parsers with local-first storage for fast repeated access.
Enables token-efficient exploration and indexing of GitHub repositories, allowing for symbol searching, file structure retrieval, and extraction of specific code definitions like functions and classes.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@github-codemunch-mcp search for the 'authenticate' function in the 'pallets/flask' repository"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
The most token-efficient MCP server for precise source code retrieval via tree-sitter AST parsing. Cut AI token costs 86-99% on code exploration (96% average, benchmarked at 28.3x fewer tokens than a grep-and-read agent) and stop burning your context window reading entire files.
Real results, live from production 838B+ tokens saved · 136,000+ reporting installs · $4.2M+ in AI spend avoided · 100,000+ kg CO₂ prevented Counter figures as of 2026-08-17, valued at the $5/MTok Claude Opus input rate. All four only grow, so read them as floors. Live at jcodemunch.com.
Works with Claude Code, Cursor, VS Code, Codex CLI, Windsurf, Continue, and any MCP-compatible client.
Install now · Quickstart · See the evidence · Pricing
Free for personal use. Use it to make money, and Uncle J. gets a taste. Fair enough? Commercial licenses below. Our guarantee: if jCodeMunch doesn't pay for itself, you don't pay for jCodeMunch.
Most AI agents explore repositories the expensive way: open entire files, skim thousands of irrelevant lines, repeat. That is not "a little inefficient." That is a token incinerator.
jCodeMunch indexes a codebase once and lets agents retrieve only the exact code they need: functions, classes, methods, constants, outlines, and tightly scoped context bundles, with byte-level precision. It parses source with tree-sitter, stores structured symbol metadata (signature, kind, qualified name, summary, byte offsets) alongside raw file content in a local index, and fetches exact implementations on demand instead of re-reading files over and over.
Task | Traditional approach | With jCodeMunch |
Find a function | Open and scan large files | Search symbol, fetch exact implementation |
Understand a module | Read broad file regions | Pull only relevant symbols and imports |
Explore repo structure | Traverse file after file | Query outlines, trees, and targeted bundles |
"What breaks if I change X?" | Not possible |
|
Index once. Query cheaply. Keep moving. Precision context beats brute-force context.
Related MCP server: cctx-mcp
Measured with tiktoken cl100k_base across three public repos pinned to upstream commits, run 2026-09-03 on v1.108.316. Workflow: search_symbols (top 5) + get_symbol_source × 3 per query. Two baselines, same run, same corpus, same file reader:
Grep-top-3: rg -l the query terms, rank files by match count, open the top 3 whole. This is what a competent agent without the tool actually does, and it is the number to quote.
Read-all: every indexed source file concatenated. A ceiling nobody pays; retained for continuity with previously published figures.
Repository | Files | Symbols | Grep-top-3 baseline | jCodeMunch | vs grep | vs read-all |
expressjs/express | 186 | 455 | 15,724 avg | 1,017 avg | 15.5x | 152.0x |
fastapi/fastapi | 1,186 | 13,240 | 85,296 avg | 2,218 avg | 38.4x | 372.0x |
gin-gonic/gin | 98 | 1,451 | 31,975 avg | 1,573 avg | 20.3x | 96.5x |
Grand total (15 task-runs) | 664,975 | 23,467 | 28.3x | 241.1x |
Against a grep-and-read agent: 96.5% reduction, 28.3x fewer tokens. Per-query results range from 7.6x to 81.2x (median 26.1x); no single multiple describes every query. Against read-all the figure is 99.6%, but nobody pays that ceiling. Compact MUNCH wire encoding then trims a median 45.5% more bytes off responses.
Full methodology, pinned commits, harness, and known caveats: benchmarks/METHODOLOGY.md · Reproduce it yourself · TOKEN_SAVINGS.md
50-iteration A/B test on a real Vue 3 + Firebase production codebase, jCodeMunch vs native tools (Grep/Glob/Read), Claude Sonnet 4.6, fresh session per iteration: success rate 80% vs 72%, timeout rate 32% vs 40%, mean cache creation down 10.5%. Tool-layer savings isolated from fixed overhead: 15-25%. One finding category appeared exclusively in the jCodeMunch variant: orphaned file detection via find_importers, a structural query native tools cannot answer without scripting. Full report: benchmarks/ab-test-naming-audit-2026-03-18.md
Artur Skowroński (VirtusLab): "roughly 80% fewer tokens, or 5× more efficient — index once, query cheaply forever" · GitHub All-Stars #15
Traci Lim (AWS · ASEAN AI Lead): "structural queries that native tools can't answer: find_importers, get_blast_radius, get_class_hierarchy, find_dead_code" · 5 Repos That Save Token Usage in Claude Code
Julian Horsey (Geeky Gadgets): "3,850 tokens reduced to just 700 — a 5.5× improvement" · JCodeMunch AI Token Saver
Eric Grill: "context is the scarce resource. Cut it by 90% and the whole stack gets cheaper and more reliable" · jCodemunch: Context Engine for AI Agents
uv tool install jcodemunch-mcp
jcodemunch-mcp initNo virtualenv to manage, nothing written into system Python, and it works as-is on PEP 668 distros (Ubuntu 24.04+, Debian 12+) where bare pip install is refused. Don't have uv yet?
init auto-detects your MCP clients (Claude Code, Claude Desktop, Cursor, Windsurf, Continue), writes their config entries, installs the CLAUDE.md prompt policy so your agent actually uses jCodeMunch, optionally installs enforcement hooks, optionally indexes your project, and audits your agent config files for token waste.
Command | Use it when |
| Zero install. Runs from an ephemeral environment — nothing lands on disk permanently. The client entries |
| You already standardise on pipx |
| Inside a virtualenv you manage yourself |
Verify:
jcodemunch-mcp --versionclaude mcp add -s user jcodemunch -- uvx jcodemunch-mcpNo install step — uvx fetches and runs the server on demand. Prefer it on your PATH (and required for enforcement hooks)? uv tool install jcodemunch-mcp, then claude mcp add -s user jcodemunch jcodemunch-mcp.
Then tell the agent to prefer the tools. This matters more than people think; installation makes the tools available but does not break the agent's brute-reading habit. One line in your CLAUDE.md does it:
Call the jcodemunch_guide tool and strictly follow its instructions.Using Cursor, Windsurf, Codex CLI, Antigravity, Gemini CLI, Qwen Code, Kiro, Cline, Zed, Goose, Hermes, Odysseus, or Paperclip? Every tested client configuration lives in CLIENTS.md. Optional extras (local semantic search, AI summaries per provider) are in QUICKSTART.md; the system surfaces each extra pulls in are documented in SECURITY.md.
Full walkthrough: QUICKSTART.md. The two-minute version, inside your agent after init:
Ask: "Index this repo with jcodemunch."
Ask: "Using jcodemunch, find the function that handles authentication and show me its source."
The agent should answer via search_symbols and get_symbol_source, returning tens of lines instead of whole files. Confirm with get_session_stats: it reports tokens served and savings for the session. That is where the numbers on the meter come from.
Want to skip initial indexing for popular frameworks? Pre-built starter packs: jcodemunch-mcp install-pack --list (free packs need no license).
Retrieve one symbol instead of loading a file. get_symbol_source returns the exact function body, byte-precise, for the majority of edits that touch one function in a 700-line file (~95% savings on that read).
Assemble a whole task's context in one call. assemble_task_context classifies the task intent, extracts anchor symbols, and runs the right tool sequence under one token budget. plan_turn routes the turn before the first read.
Ask structural questions grep can't answer. find_importers, get_blast_radius, get_call_hierarchy, find_dead_code, get_changed_symbols, get_hotspots, search_ast anti-pattern sweeps, and more.
Preflight risky changes, and know when to stop. check_edit_safe, check_delete_safe, get_pr_risk_profile, and plan_refactoring with edit-ready {old_text, new_text} blocks. The two safety checks return stop_rule.terminal: true means no further jcodemunch call moves the verdict, so re-running find_importers or check_references to be sure is wasted work. It means final, not safe. False names the specific thing that would change the answer.
Trust the answers. Calibrated confidence scores, freshness flags, coverage contracts on absence claims, compiler-verified references via SCIP import, and automatic secret redaction before anything reaches the LLM.
Keep the index fresh automatically. Watch modes, agent hooks, and a VS Code extension close the staleness gap.
That's the highlight reel. The complete tour of 90+ tools, the MUNCH compact wire format, evidence receipts, offloadable-work annotation, and the session-economics instrumentation is in CAPABILITIES.md, with internals in UNDER_THE_HOOD.md.
v1.108.316 (2026-09-02) — A display preference edited the data it was displaying
v1.108.315 (2026-09-01) — A fix for a false positive can install a false negative
v1.108.314 (2026-09-01) — A rate written for a future date is wrong for every day before it
Scenario | Native tool | jCodeMunch | Savings |
Edit one function (700-line file) |
|
| ~95% |
Understand a file's structure |
|
| ~80% |
Find which file to edit |
|
| comparable |
Edit requires whole-file context |
|
| ~0% |
"What breaks if I change X?" | not possible |
| unique capability |
It helps most on targeted edits (one function, one method, one class), which is the majority of real editing work. Edits that genuinely require the entire file (restructuring file-level state, reordering logic spanning hundreds of lines) see no advantage. Best fits: large repositories, unfamiliar codebases, agent-driven exploration, refactoring and impact analysis, and teams cutting AI token costs without making agents dumber.
Languages: 70+ via tree-sitter, including Python, JavaScript/TypeScript, Go, Rust, Java, C/C++, C#, PHP, Ruby, Swift, and Kotlin. Full matrix: LANGUAGE_SUPPORT.md. Monorepos: yes; incremental indexing, workspace-member detection, subpath scoping.
If you reach jCodeMunch through the MCP connector on a model that supports tool search, you can keep our schemas out of your context prefix entirely and let Claude load only the two or three tools a request needs. You do not set defer_loading per tool — set it once for the whole server:
{
"mcp_servers": [
{ "type": "url", "url": "https://your-host/mcp", "name": "jcodemunch" }
],
"tools": [
{ "type": "tool_search_tool_bm25_20251119", "name": "tool_search_tool_bm25" },
{
"type": "mcp_toolset",
"mcp_server_name": "jcodemunch",
"default_config": { "defer_loading": true },
"configs": {
"resolve_repo": { "defer_loading": false },
"search_symbols": { "defer_loading": false },
"get_ranked_context": { "defer_loading": false }
}
}
]
}Send it with the beta header mcp-client-2025-11-20. Both halves are required — mcp_servers alone is a validation error, and so is mcp_toolset without the matching mcp_server_name.
⚠ The MCP connector takes a URL, so this applies to jCodeMunch served over sse or streamable-http (jcodemunch-mcp serve --transport streamable-http), not to the default local stdio setup. On stdio, whether schemas are deferred is up to your client, and tool_surface: "counter" below is the lever you control.
The configs block above follows Anthropic's own advice — keep your 3–5 most-used tools resident so common requests skip the search round trip — and per-tool configs overrides default_config.
Deferred definitions are excluded from the system-prompt prefix and appended inline as tool_reference blocks when Claude discovers them, so prompt caching is preserved — this is not the cache-invalidating kind of dynamic tool list. At least one tool in the request must stay non-deferred, or the API returns a 400.
⚠ This is a different mechanism from our own tool_surface: "counter", and you do not need both. Tool search is host-side and works across every MCP server you have connected; the Counter is server-side, works on any host including ones with no tool-search support, and is what init configures on a first-ever install. Pick whichever your host supports — see CONFIGURATION.md for the Counter and jcodemunch-mcp surface for what your install actually advertises.
Local-first by design: indexes live at ~/.code-index/, and the base package's only default network behavior is an anonymous savings counter (random ID plus aggregate token counts, no code, no paths, no PII; opt out with share_savings: false). Everything the server does beyond answering a tool call (file watching, the opt-in login service, license validation, model downloads, org reporting) is opt-in or opt-out, visible, and reversible, and every item is enumerated in SECURITY.md alongside the path-traversal, symlink, and secret-redaction controls.
Most settings live in the global ~/.code-index/config.jsonc, but any of them can be overridden for a single repository by dropping a .jcodemunch.jsonc at its root. It is an overlay: keys it declares win, keys it omits fall through to global and then to the built-in default, so it only needs to contain what differs.
// <your-repo>/.jcodemunch.jsonc
{
"max_file_size": 1048576,
"languages": ["python", "typescript", "racket"]
}Racket projects routinely define their own defining forms with define-syntax, and a static parser cannot know what those bind — (defstep (check-admin) ...) is indistinguishable from a function call. Declaring them makes their bindings searchable:
{
"racket_definition_forms": {
"defstep": "function",
"defstudy": "constant",
"defvar": "constant",
"define-schema": "class"
}
}Each entry maps a form name to what it binds: function, constant, class or type. Where the name sits is read from the source rather than declared — (defstep (check-admin) ...) takes the head of the parameter list, (defstudy consent ...) takes the bare symbol — so a form that appears in both shapes works either way.
⚠ This is an assertion, not something jCodeMunch can verify. A wrong declaration puts a name in the index that Racket does not actually bind. Declarations are also matched only after every built-in form, so declaring define or struct has no effect — the built-in handling wins.
#lang looks likeA #lang line names a reader, and jCodeMunch's Racket parser reads S-expressions. The distribution's langs are built in (racket/*, typed/racket*, s-exp, info, at-exp …, and the document langs scribble/*, pollen, punct, markdown …), but a project's own lang is unknown to it and is treated as a document — no symbols, still text-searchable — until you say what its syntax is:
{
"racket_langs": {
"conscript": "at-exp",
"mylang": "sexp"
}
}sexp is plain S-expressions; at-exp is at-exp text bodies over Racket (read with @ as the command character, exactly as #lang at-exp reads them, so prose containing ; " # or | is prose); text is a document language that is never walked. A key also covers its sub-langs (conscript matches conscript/with-require), and a project may demote a lang as well as promote one. An at-exp lang whose reader uses another command character declares it with the object form — "mylang": {"tier": "at-exp", "command_char": "◊"} — the way Racket's make-at-readtable takes #:command-char.
Both keys change what the parser emits for unchanged files, so a change to either is stamped on the index and forces one full re-parse on the next index (rebuild_reason: "racket_config_changed"); you do not need to touch the files or clear the index. An index holding Racket files that was built before this stamp existed re-parses once the same way (rebuild_reason: "racket_index_predates_gate").
Doc | What it covers |
Zero-to-indexed in three steps | |
Tested configuration for every MCP client | |
Full tool reference, workflows, and best practices | |
The complete capability reference beyond the highlight reel | |
Config file reference, token-control levers, tool tiering, the Counter | |
The technical manual: verdicts, ranking internals, provenance contracts | |
Internal design, storage model, and extension points | |
Groq Remote MCP, the gcm CLI, speedreview GitHub Action | |
Using jCodeMunch with | |
Agent hooks and prompt policies | |
Supported languages and parsing details | |
Security controls, data movement, background behavior | |
Common issues and fixes | |
Release history and what's next |
jCodeMunch-MCP is released under the jCodeMunch-MCP Dual-Use License (full terms). Free for non-commercial use. Commercial use requires a paid license, one-time, sold by jMunch LLC via Stripe:
jCodeMunch-only: Builder, $79 (1 developer) · Studio, $349 (up to 5) · Platform, $1,999 (org-wide internal deployment)
Full jMunch suite (code + docs + data): Trio Builder, $99 · Trio Studio, $449 · Trio Platform, $2,499
Not sure it's worth it? Run your own numbers through the ROI calculator, or forward the finance-team version to whoever signs off. The guarantee stands: if jCodeMunch doesn't pay for itself, you don't pay for jCodeMunch.
Conditions on all uses: retain the copyright notice, clearly mark modifications and keep the original author's name intact (he's kinda full of himself), and include a prominent modification notice in source redistributions. The Software may not be renamed, rebranded, or published to any public package registry, and is provided "AS IS" without warranty. LICENSE controls.
How much can I save on Claude / Opus tokens? In retrieval-heavy workflows, code-reading tokens typically drop 86-99%, benchmarked at 96.5% average (28.3x) against a grep-and-read agent across 15 tasks and 3 repositories. Per-query results span 7.6x to 81.2x. Methodology: TOKEN_SAVINGS.md and benchmarks/.
How is this different from RAG or grep-based tools? jCodeMunch retrieves at the symbol level with byte-level precision (functions, classes, importers, blast radius, hierarchies) rather than fuzzy chunks (RAG) or raw line matches (grep) the agent still has to read and reason over.
Is it free for personal use? Yes. Commercial use needs a license; see above.
Where's the deep-dive on X? Capabilities: CAPABILITIES.md. Config: CONFIGURATION.md. Clients: CLIENTS.md. Internals: UNDER_THE_HOOD.md. Or the firehose: jcodemunch.com.
Extras: OSS code-health observatory (weekly six-axis snapshots of Express, FastAPI, Gin, Django, and friends) · Token Cost Radar (daily AI token cost intelligence) · jMunch Console (free MIT GUI for one-click upgrades)
Get all symbols (functions, classes, methods) in a file with signatures and summaries.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | Repository identifier (owner/repo or just repo name) | |
| file_path | Yes | Path to the file within the repository (e.g., 'src/main.py') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions what information is returned (signatures and summaries), it doesn't address important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or response format details. The description provides basic output information but misses critical operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-constructed sentence that efficiently conveys the core functionality without any wasted words. It's front-loaded with the main purpose and includes essential details about the output format. Every element earns its place in this concise formulation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no annotations and no output schema, the description provides basic purpose and output information but lacks sufficient operational context. It doesn't explain what format the symbols are returned in, how errors are handled, or any limitations of the tool. While concise, it leaves important gaps for a tool that presumably returns complex symbol data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already fully documents both parameters (repo and file_path). The description doesn't add any parameter-specific information beyond what's in the schema, such as format examples for repo beyond 'owner/repo' or edge cases for file_path. The baseline score of 3 reflects adequate but minimal value addition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get all symbols'), resource ('in a file'), and scope ('with signatures and summaries'), distinguishing it from siblings like get_file_tree (file structure) or get_symbol (single symbol). It uses precise terminology that helps the agent understand exactly what this tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for extracting symbol information from a specific file, but doesn't explicitly state when to use this vs. alternatives like get_symbol (single symbol) or search_symbols (search across files). No guidance on prerequisites or exclusions is provided, leaving usage context somewhat ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the file tree of an indexed repository, optionally filtered by path prefix.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | Repository identifier (owner/repo or just repo name) | |
| path_prefix | No | Optional path prefix to filter (e.g., 'src/utils') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the repository must be 'indexed' (a key behavioral constraint) and describes optional filtering. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, what format the file tree returns (e.g., hierarchical structure), potential rate limits, or error conditions (e.g., if repo isn't indexed). For a tool with no annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get the file tree of an indexed repository') and adds qualifying information ('optionally filtered by path prefix'). Every word earns its place with zero waste, making it easy for an AI agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and filtering but lacks details on return format, error handling, and behavioral constraints. Without annotations or output schema, the agent must infer these from context, leaving room for misinterpretation. A score of 3 reflects this borderline completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('repo' and 'path_prefix') with clear descriptions. The description adds marginal value by reinforcing the optional filtering concept ('optionally filtered by path prefix') but doesn't provide additional semantic context beyond what's in the schema. With high schema coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'file tree of an indexed repository' with optional filtering. It distinguishes from siblings like 'get_file_outline' (which likely provides different structural information) and 'get_symbol' (which focuses on specific symbols rather than file structure). However, it doesn't explicitly differentiate from 'list_repos' which lists repositories rather than their file trees.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning 'indexed repository' (suggesting the repo must be indexed first) and 'optionally filtered by path prefix' (suggesting when filtering is appropriate). However, it doesn't provide explicit guidance on when to use this versus alternatives like 'get_file_outline' or 'search_symbols', nor does it specify prerequisites beyond the repository being indexed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the full source code of a specific symbol. Use after identifying relevant symbols via get_file_outline or search_symbols.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | Repository identifier (owner/repo or just repo name) | |
| symbol_id | Yes | Symbol ID from get_file_outline or search_symbols |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the tool retrieves 'full source code,' which implies a read-only operation, but doesn't disclose behavioral traits like error handling, performance limits, or response format. While it adds some context about usage timing, it lacks details on what happens if the symbol_id is invalid or if the repo is inaccessible.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence earns its place by providing essential information without waste, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is adequate but has gaps. It covers purpose and usage well, but without annotations or output schema, it lacks details on behavioral aspects like what the returned source code includes (e.g., formatting, metadata) or potential errors. It's minimally viable but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (repo and symbol_id) with descriptions. The description adds no additional meaning beyond implying symbol_id comes from specific sibling tools, but this is covered in usage guidelines rather than parameter semantics. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get the full source code') and resource ('a specific symbol'), distinguishing it from siblings like get_file_outline (which provides outlines) and search_symbols (which searches for symbols). It precisely defines the tool's function without being vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('Use after identifying relevant symbols via get_file_outline or search_symbols'), providing clear guidance on prerequisites and distinguishing it from alternatives. This helps the agent understand the workflow and avoid misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get full source code of multiple symbols in one call. Efficient for loading related symbols.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | Repository identifier (owner/repo or just repo name) | |
| symbol_ids | Yes | List of symbol IDs to retrieve |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions efficiency ('Efficient for loading related symbols'), which is a useful trait, but lacks critical details: it doesn't specify if this is a read-only operation, potential rate limits, error handling (e.g., for invalid symbol IDs), or what happens if some symbols aren't found. For a tool with 2 required parameters and no annotations, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, and the second adds efficiency context. Both sentences earn their place with no wasted words, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is minimally complete. It covers the purpose and efficiency but lacks details on behavior, error handling, or output format. Without annotations or output schema, more context would be helpful, but it's not entirely inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for both parameters ('repo' and 'symbol_ids'). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get full source code of multiple symbols in one call.' It specifies the verb ('Get'), resource ('full source code of multiple symbols'), and scope ('in one call'). However, it doesn't explicitly differentiate from sibling tools like 'get_symbol' (singular) or 'search_symbols', which would require a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidance: 'Efficient for loading related symbols' suggests this tool is optimal for batch retrieval when symbols are related. It doesn't explicitly state when to use this vs. alternatives like 'get_symbol' (for single symbols) or 'search_symbols' (for finding symbols), nor does it mention exclusions, so it falls short of a 4 or 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Index a GitHub repository's source code. Fetches files, parses ASTs, extracts symbols, and saves to local storage.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | GitHub repository URL or owner/repo string | |
| use_ai_summaries | No | Use AI to generate symbol summaries (requires ANTHROPIC_API_KEY). When false, uses docstrings or signature fallback. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the multi-step process (fetching, parsing, extracting, saving) and mentions local storage persistence, which adds useful context. However, it lacks details on permissions, rate limits, error handling, or what happens if the repository is already indexed, leaving gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose and key steps without unnecessary details. It is front-loaded with the main action ('Index a GitHub repository's source code') and every clause adds value, making it highly concise and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (indexing with AI options) and lack of annotations or output schema, the description is moderately complete. It outlines the process and storage outcome but omits details on performance, side effects, or return values. For a mutation tool with no output schema, more behavioral context would improve completeness, but it meets minimum viability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description does not add specific parameter semantics beyond what the schema provides, but since there are only 2 parameters and the schema is comprehensive, a baseline of 3 is appropriate. The description's mention of AI summarization aligns with the use_ai_summaries parameter, slightly enhancing understanding, warranting a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('index', 'fetches', 'parses', 'extracts', 'saves') and resources ('GitHub repository's source code', 'files', 'ASTs', 'symbols', 'local storage'). It distinguishes from siblings like get_file_outline or search_symbols by emphasizing the comprehensive indexing process rather than retrieval or search operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for initial indexing of a repository, but does not explicitly state when to use this tool versus alternatives like list_repos or get_file_tree. It mentions AI summarization as an option, which provides some context, but lacks clear guidance on prerequisites or exclusions compared to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all indexed repositories.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read-only operation by using 'List', but doesn't specify details such as pagination, rate limits, authentication needs, or what 'indexed' entails (e.g., recently updated vs. all-time). This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is front-loaded and appropriately sized for a simple tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate but lacks depth. It doesn't explain what 'indexed' means, the format of the returned list, or how this differs from sibling tools. For a list operation, more context on scope and output would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter details, but it could have mentioned implicit constraints (e.g., no filtering options). A baseline of 4 is appropriate as it avoids redundancy.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('indexed repositories') with the scope 'all', making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_file_tree' or 'search_symbols', which might also involve repository listings, so it misses the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_file_tree' or 'search_symbols', nor does it mention any prerequisites or context for usage. It simply states what the tool does without indicating appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for symbols matching a query across the entire indexed repository. Returns matches with signatures and summaries.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | Repository identifier (owner/repo or just repo name) | |
| query | Yes | Search query (matches symbol names, signatures, summaries, docstrings) | |
| kind | No | Optional filter by symbol kind | |
| file_pattern | No | Optional glob pattern to filter files (e.g., 'src/**/*.py') | |
| max_results | No | Maximum number of results to return |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the search scope ('across the entire indexed repository') and return content ('matches with signatures and summaries'), but lacks details on permissions, rate limits, pagination, or error handling. For a search tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two clear sentences that efficiently convey the core functionality and return value. There is no wasted language, and every sentence earns its place by adding essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and return content, but lacks usage guidelines, behavioral details, and output specifics. Without annotations or an output schema, more context on behavior and results would be beneficial for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the query matches 'symbol names, signatures, summaries, docstrings' (which is covered in the schema's query description) and mentioning 'returns matches,' but does not provide additional syntax or format details. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for symbols matching a query across the entire indexed repository.' It specifies the verb ('Search'), resource ('symbols'), and scope ('across the entire indexed repository'), but does not explicitly differentiate it from sibling tools like 'get_symbol' or 'get_symbols', which appear related.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions returning 'matches with signatures and summaries,' but does not indicate when this search is preferred over sibling tools such as 'get_symbol' (likely for a specific symbol) or 'get_symbols' (possibly for all symbols). No exclusions or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most tools have distinct purposes, but get_symbol and get_symbols could be confused as they both retrieve symbol source code, differing only in single vs. multiple symbols. The descriptions clarify this, but the naming similarity creates minor ambiguity. Other tools like get_file_outline, get_file_tree, and search_symbols are clearly differentiated.
All tool names follow a consistent verb_noun pattern with snake_case, such as get_file_outline, index_repo, and search_symbols. There are no deviations in naming conventions, making the set predictable and easy to parse for agents.
With 7 tools, the count is well-scoped for the server's purpose of indexing and querying GitHub repository code. Each tool serves a specific function in the workflow, from indexing and listing repos to retrieving file structures and symbols, without unnecessary redundancy.
The toolset covers core workflows for code indexing and symbol retrieval, including repository management, file exploration, and symbol search. A minor gap exists in update or delete operations for indexed repos, but agents can work around this, and the surface is largely complete for the stated domain.
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