How do I use mcp-intelligence-context?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-intelligence-context What imports and dependencies does the auth module have?" 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.

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mcp-intelligence-context

by LeoChimal09

Overview Schema Source Related Servers Discussions Admin

Python

Local

MCP Intelligence Context

A Repository Intelligence MCP server that indexes a codebase's files, symbols, imports and dependency graph, and hands Copilot/agents a small, focused context package instead of making them scan the entire repository.

Why

When an agent gets an ambiguous question about a large repo, it often has to repeatedly list directories, open unrelated files, and re-derive structure before finding the relevant code — burning tokens and time. This project builds a persistent, incrementally-updated index of the repo (files, symbols, imports, reverse dependencies) and exposes MCP tools that return only the context relevant to a query, with an approximate token budget.

Related MCP server: lens

How it works

index_repository walks the repo (honoring .gitignore), parses Python (via ast) and JS/TS (via lightweight regex heuristics) files for functions/classes/methods/imports/exports, and builds a reverse dependency graph. The index is cached at .mcp_intel_cache/index.json and refreshed incrementally (only changed files are re-parsed, based on mtime/size).
search_code / get_relevant_context rank files by symbol-name, filename, docstring/summary, and import matches (lexical/symbol search — no embeddings in this MVP) and return a token-budgeted context package: symbol tables + small code excerpts, not whole files. get_relevant_context also reports a token_savings comparison against a naive full-repo-scan baseline, so the savings are visible in the tool's own response.
get_file_summary / get_dependencies let an agent drill into a specific file's symbols or blast radius (importers/imports) without reading the whole file.
Tools report a staleness warning if the cached index is older than 5 minutes and no live watcher is active. In practice, the first tool call for a repo starts a background file watcher (via watchdog) that applies create/modify/delete events to the in-memory index immediately, so the index stays continuously up to date as the code changes — no manual reindex needed during a session. The on-disk cache is flushed on a debounce (~2s) so rapid saves don't cause a write per keystroke.

Repository layout

src/mcp_intelligence_context/   Python MCP server package
  walker.py                     gitignore-aware file walker
  parsers/                      Python (ast) and JS/TS (regex) symbol extraction
  indexer.py                    builds/caches the RepoIndex, resolves imports
  watcher.py                    background file watcher that keeps the index live
  search.py                     lexical/symbol search + reverse-dep lookups
  context_builder.py            token-budgeted context package assembly
  server.py                     MCP tool definitions (stdio server)
vscode-extension/                VS Code extension wrapper (setup/reindex/status commands)
scripts/                         one-command bootstrap for new users

Quick Start (New Users)

If you are new to MCP and just want this working in VS Code quickly:

git clone https://github.com/LeoChimal09/MCP-INTELLIGENCE-CONTEXT.git
cd MCP-INTELLIGENCE-CONTEXT
bash scripts/setup_mcp_workspace.sh

What this script does:

Installs (or updates) mcp-intelligence-context with pipx.
Writes .vscode/mcp.json for this workspace.
Restricts indexing to the current workspace folder by setting MCP_INTEL_ALLOWED_ROOTS=${workspaceFolder}.

Then in VS Code:

Command Palette -> MCP: List Servers.
Start/Restart mcp-intelligence-context.
In Copilot Chat tool picker, enable mcp-intelligence-context.

If the script says pipx is missing, install it once:

brew install pipx
pipx ensurepath

Running the MCP server standalone

python3 -m venv .venv
.venv/bin/pip install -e .
.venv/bin/mcp-intelligence-context        # or: python -m mcp_intelligence_context.server

Point the repo to index by setting MCP_INTEL_REPO_ROOT, or pass repo_root explicitly to any tool call (defaults to the server's current working directory).

Installing without cloning this repo

Other users don't need a local checkout — install directly from the git repository (or from PyPI, once published there):

python3 -m venv .venv
.venv/bin/pip install "git+https://github.com/LeoChimal09/MCP-INTELLIGENCE-CONTEXT.git"
# once published: .venv/bin/pip install mcp-intelligence-context

The mcp-intelligence-context console script and MCP_INTEL_REPO_ROOT env var work exactly the same either way — only the pip install source differs.

Register with an MCP client (e.g. VS Code)

Add to .vscode/mcp.json in the target workspace:

{
  "servers": {
    "mcp-intelligence-context": {
      "type": "stdio",
      "command": "/absolute/path/to/.venv/bin/python",
      "args": ["-m", "mcp_intelligence_context.server"],
      "env": { "MCP_INTEL_REPO_ROOT": "${workspaceFolder}" }
    }
  }
}

VS Code extension

vscode-extension/ bundles a thin wrapper with three commands:

MCP Intelligence: Setup Server — creates a venv and installs the Python package, then writes the .vscode/mcp.json entry above.
MCP Intelligence: Reindex Repository — forces a re-index of the open workspace.
MCP Intelligence: Show Status — prints the cached index's file count, git commit, and age.

By default, "Setup Server" installs the package from this project's git repository into a venv under the extension's private storage — no local clone required. Two settings control this:

mcpIntelligenceContext.serverPath — point at a local editable checkout (used for development on this monorepo); leave empty otherwise.
mcpIntelligenceContext.pythonPackageSource — override the pip install target (e.g. a PyPI package name) when serverPath is empty.

To build it:

cd vscode-extension
npm install
npm run compile

Then press F5 in VS Code (with vscode-extension/ open) to launch an Extension Development Host.

Available MCP tools

Tool	Purpose
`index_repository`	Build/refresh the index for a repo root
`get_repo_overview`	Top-level directories, language breakdown, core modules
`search_code`	Ranked file/symbol hits for a query
`get_file_summary`	Symbol table, imports, exports for one file
`get_dependencies`	What a file imports and who imports it
`get_relevant_context`	Token-budgeted context package for a query, plus a `token_savings` estimate vs. a naive full-repo scan

Evaluating whether this actually helps

eval/ contains a small, honest benchmark against this repo's own code (no LLM calls, no fabricated numbers): 10 hand-written queries with known ground-truth files, comparing our indexed tool against a naive baseline (list the tree, grep, read whole matching files).

.venv/bin/python eval/run_eval.py

It reports hit@1/hit@3 (does the top result point at the right file), average token reduction, and latency. This only measures retrieval/token mechanics — it does not measure whether a real Copilot answer is actually better, since that requires live model calls.

Current limitations (MVP)

JS/TS parsing is regex-based (not a full AST), so unusual syntax may be missed. Python parsing uses the standard ast module and is exact.
Search is lexical/symbol-based only (with stopword filtering and accumulated multi-signal scoring); no embeddings/semantic search yet.
The file watcher applies per-file changes but does not re-walk .gitignore changes themselves at runtime — if .gitignore is edited, run index_repository with refresh=true once to pick up the new rules.

Security considerations before broader/production use

Already fixed:

Shell injection — the VS Code extension previously interpolated workspace settings into shell command strings; it now uses execFile with argument arrays (no shell), and refuses to run "Setup Server" in untrusted workspaces.
Symlink escape — the walker skips symlinks that resolve outside the repo root (blocks a planted symlink from exposing files like /etc/passwd).
Secret leakage — filenames matching common credential patterns (.env, *.pem, id_rsa, credentials.json, etc., see SENSITIVE_FILENAME_PATTERNS in config.py) are skipped even if not gitignored, so their contents can't end up in tool output.
Corrupted-cache crash — a malformed/tampered .mcp_intel_cache/index.json now triggers a clean rebuild instead of crashing the server on launch.
ReDoS — the JS/TS regex parser skips pathologically long single lines (minified files) to avoid catastrophic-backtracking DoS.
Unrestricted repo_root — set MCP_INTEL_ALLOWED_ROOTS (a :-separated list of absolute paths) to restrict which directories the server will index; unset by default to preserve today's flexible single-user behavior.

Still architectural, not fully solved — read before deploying beyond a single local user:

Not safe as a shared/multi-tenant network service. This is designed as a local, one-process-per-user stdio server. The in-memory index/watcher caches have no per-user isolation or authentication. Do not expose this as a shared HTTP/SSE endpoint without adding per-caller sandboxing and auth.
Dependencies are unpinned (>= only) — pin exact versions or use a lock file for reproducible, vetted production installs (this already bit us once with an mcp 1.x → 2.0 breaking API change).
No automated regression tests for this codebase itself yet — changes are currently verified via the manual eval/ harness and ad hoc runs, not a CI-gated test suite.

Available Tools

6 tools

get_dependenciesA

Return what a file imports and which files import it (reverse deps), to understand blast radius before changing a file.

ParametersJSON Schema

Name	Required	Description	Default
`path`	Yes
`repo_root`	No

TDQS

A3.5/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It states the operation is read-only ('return what a file imports...'), which is helpful, but it doesn't disclose whether the tool scans the whole repo, handles dynamic imports, or has performance implications. Given the lack of annotations, a 3 is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no fluff. The main purpose is stated first, and the use case is added compactly. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 2 params and no output schema, the description covers the primary use case, but it lacks details on repo_root semantics, return format, and any edge cases (like missing files). The complexity is moderate, so this is incomplete but not severely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, and the description does not explain either parameter. The path parameter is obvious from the tool name, but repo_root is undocumented. With no parameter info, a lower score might be expected, but the description's mention of 'file' implies path usage; still, the gap is significant.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns a file's imports and reverse dependencies, with a specific purpose (understanding blast radius). It distinguishes itself from siblings like search_code and get_file_summary by focusing on dependency relationships.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage ('to understand blast radius before changing a file') but does not explicitly state when not to use it or mention alternatives like get_relevant_context for broader context. The context signals show siblings, but no explicit routing guidance is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_file_summaryB

Return the symbol table (functions/classes/methods with signatures and docstrings), imports, and exports for a single indexed file — without returning the entire file body.

ParametersJSON Schema

Name	Required	Description	Default
`path`	Yes
`repo_root`	No

TDQS

B3.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral burden. It discloses the main output content (symbol table, imports, exports) and a notable exclusion (not returning file body). However, it does not mention prerequisites like requiring the file to already be indexed or what happens when the file is missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single well-structured sentence with no filler. It front-loads the core action and contents, then adds the clarifying exclusion. Every part contributes to understanding the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, no annotations, and zero parameter documentation, the description alone is not enough for an agent to confidently invoke this tool. It explains what is returned but not parameter semantics, indexing prerequisites, or error behavior, leaving important gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage and the description does not explain 'path' or 'repo_root' semantics. While 'single indexed file' loosely ties 'path' to a file location, 'repo_root' remains unexplained, and no format, relative/absolute path guidance, or repository boundary details are provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Return') and names a concrete resource ('symbol table... imports, and exports') for a single indexed file. It clearly conveys what the tool does and even specifies what it doesn't do ('without returning the entire file body'), though it doesn't explicitly distinguish itself from siblings like get_relevant_context or get_repo_overview.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'for a single indexed file' implies the tool is for per-file summaries, but there is no explicit guidance on when to prefer this over search_code, get_dependencies, or get_relevant_context. No exclusions or alternatives are mentioned, so usage context is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_relevant_contextA

Build a focused, token-budgeted context package (symbol tables + small code excerpts) for the files most relevant to a query. This is the main entry point for "help me find/fix X" style requests — use it instead of reading whole files or globbing the repo.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`repo_root`	No
`token_budget`	No

TDQS

A3.5/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description risks the full burden of behavioral disclosure. It communicates that the tool returns a synthesized context package rather than raw files, and that it is token-limited, but it does not state whether it is strictly read-only, whether it depends on an existing repository index, or what the actual response format looks like.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured. The first sentence states the core action and output, and the second provides routing guidance. There is no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives enough to orient an agent to the main purpose and broad usage, but it leaves gaps around the return structure, required prior state (e.g., indexation), and parameter behaviors. Because there is no output schema, more detail about the package shape would help.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not explain the three parameters. 'query' and 'token-budgeted' are lightly implied, but repo_root is entirely undocumented, meaning an agent cannot infer the expected input format or behavior for that parameter without external knowledge.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool builds a focused, token-budgeted context package of symbol tables and code excerpts for relevant files. It distinguishes itself as the 'main entry point' for find/fix requests, which differentiates it from lower-level or broader repository tools, though it does not explicitly name all sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to use this tool for 'help me find/fix X' requests and to use it instead of reading whole files or globbing the repository. This provides a clear when and when-not, but it does not name specific alternative tools such as search_code or get_file_summary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_repo_overviewA

Return a high-level architecture summary: top-level directories, language breakdown, and the files with the most symbols (likely core modules). Use this to orient before drilling into specific files.

ParametersJSON Schema

Name	Required	Description	Default
`repo_root`	No

TDQS

A3.8/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the read-only nature by saying 'Return' and describes the output shape, but it does not mention potential prerequisites, performance implications, or what happens when repo_root is null. This is adequate but incomplete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with no filler. The first sentence front-loads the core behavior and output content; the second gives the usage context. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only tool with one optional parameter and no output schema, the description covers the main purpose and output well. However, the undefined parameter behavior and the relationship to index_repository leave enough ambiguity that an agent may not know how to invoke it correctly on first use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage for repo_root, and the tool description does not mention the parameter at all. The agent is left to infer that repo_root is the repository path and that null may mean the current directory, which the description does not clarify.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states exactly what the tool returns: a high-level architecture summary with top-level directories, language breakdown, and files with the most symbols. This clearly distinguishes it from sibling file-level tools like get_file_summary and search_code.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to use this to 'orient before drilling into specific files,' which gives a clear when-to-use context. It does not name alternatives or state when not to use it, but the orientation purpose is specific enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

index_repositoryA

Index (or re-index) a repository: extracts files, symbols, imports and a reverse dependency graph. Call this first, and again with refresh=true after significant file changes. Uses an on-disk cache for fast re-runs.

ParametersJSON Schema

Name	Required	Description	Default
`refresh`	No
`repo_root`	No

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavior disclosure, and it does reasonably well: it declares persistence behavior ('on-disk cache for fast re-runs'), signals re-indexing with refresh=true, and describes the data that is extracted. It stops at details like what the tool returns or whether it is non-destructive, but the core behavioral traits are covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, no filler: the first defines what it does, the second gives the call-order rule applied, and the third explains caching. Important behavioral information is front-loaded, and every clause adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a simple two-parameter schema with no output schema and no annotations, the description is close to complete: it asserts when to call it for the first time, when to re-run with refresh, what the internal behavior is (dependency graph building and caching), and what gets updated. It could specify what happens after indexing or what success looks like, but it is complete enough for this kind of setup tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does explain refresh past the schema by tying it to re-running after file changes. However, repo_root is never mentioned or defined; an agent can infer it is the repository path, but the uncertainty is left to guesswork. Since at least one parameter is semantically enriched and the other is left implicit, this is adequate but not strong.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action and resource: 'index (or re-index) a repository' and enumerates what it extracts — files, symbols, imports, and a reverse dependency graph. It reads as an explicit foundation step, and the siblings (get_repo_overview, search_code, get_dependencies) are clearly different read/query tools rather than indexing tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description says 'Call this first, and again with refresh=true after significant file changes.' This is direct when-to-use guidance, making the invocation very and rerun conditions clear. It does not explicitly contrast with the sibling tools, but the relationship is strongly implied: this is the preliminary indexing step before querying with the get_* siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_codeA

Search the indexed repository for files/symbols relevant to a natural language or keyword query (e.g. "authentication middleware", "parseConfig"). Returns ranked file hits with the matching symbol/reason — much cheaper than scanning the whole repo.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No
`repo_root`	No

TDQS

A3.5/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It does reveal the main output behavior—ranked file hits with matching symbol/reason—and the cost advantage. However, it does not mention whether the repository must already be indexed, how repo_root is scoped, or any failure/edge behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: it states the action, example usage, return behavior, and cost benefit in two efficient sentences. There is no fluff or redundant restatement of the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a fairly simple tool and the description covers the core purpose and return shape adequately. Still, it leaves important invocation details undocumented, especially the semantics of top_k and repo_root, plus the implied prerequisite that the repository must be indexed first.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds useful meaning for the query parameter with natural-language/keyword guidance and examples, but top_k and repo_root are not explained at all. Since schema description coverage is 0%, the description was expected to compensate for all parameters but only covers one meaningfully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches the indexed repository for files/symbols matching a natural language or keyword query and returns ranked file hits with matching symbols/reasons. It is specific about the resource and output, though it does not explicitly distinguish it from siblings like get_relevant_context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when to use the tool: to find relevant files/symbols via semantic or keyword search. It also notes the tool is much cheaper than scanning the whole repo, which is useful guidance, though no explicit when-not-to-use or alternative routing is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

6 tool updatesv0.1.0Aug 23, 2026
- First observedget_dependencies
- First observedget_file_summary
- First observedget_relevant_context
- First observedget_repo_overview
- First observedindex_repository
- First observedsearch_code

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored 2026-08-23 00:25 across 6 tools

Disambiguation4/5

Each tool has a largely distinct purpose (index, overview, search, file details, dependencies, context), and the descriptive text flags get_relevant_context as the main entry point. However, search_code and get_relevant_context could both serve as the starting point for a 'find X' request, creating minor selection ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern (index_, get_, search_), making the API predictable and scannable. The only minor nitpick is abbreviating 'repository' as 'repo' in get_repo_overview, but this does not affect the overall pattern.

Tool Count5/5

Six tools sit at the sweet spot for a code-intelligence server, each earning its place in the index-query workflow. The set covers setup, orientation, discovery, drill-down, and impact analysis without bloat.

Completeness4/5

The core indexing-to-context pipeline is well covered, including re-indexing via refresh and reverse-dependency analysis for safe edits. Minor gaps exist (no full-file body reader, no way to list indexed repositories or explicitly purge the cache), but these seem like intentional scope decisions for a token-budget-focused tool.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

GitHub Repository

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maintenance
Enables LLM agents to efficiently understand and navigate a codebase by providing semantic search over symbols and a reference graph, replacing expensive grep/glob calls with structured tools like definition lookup, caller/callee queries, and change-impact analysis.
Updated 2 months ago (2026-07-21 19:03 UTC)
2
MIT
codegraph-mcp
Code Analysis Developer Tools RAG Systems
chainchen1103
F
license
Not graded
quality
A
maintenance
Provides efficient code navigation and graph-based analysis for AI agents, enabling symbol resolution, callers, implementations, and type schemas with minimal token usage.
Updated 2 days ago (2026-09-11 09:29 UTC)
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