token-context-mcp
This server provides token-efficient code intelligence over registered repositories.
List registered repository IDs.
Generate ranked repository maps within a token budget.
Find symbols by name or qualified-name pattern.
Search source with optional call-graph expansion.
Inspect file skeletons, imports, and headers without function bodies.
Analyze lexical module dependents and callers/callees.
Traverse candidate impact slices from a symbol.
Check index status, snapshot metadata, and changed paths.
Inspect a symbol with its immediate graph, signatures, body, and related tests in one composite call.
Provides hardware-aware LLM sampling via Ollama auto-routing, enabling summarization tasks to be delegated to a local Ollama instance with deterministic heuristic fallback.
Click on "Deploy 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., "@token-context-mcpProvide a symbol overview of the video-lecturer repo"
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.
MCP Registry
The MCP registry provides MCP clients with a list of MCP servers, like an app store for MCP servers.
π€ Publish my MCP server | β‘οΈ Live API docs | π Ecosystem vision | π Full documentation
Development Status
2025-10-24 update: The Registry API has entered an API freeze (v0.1) π. For the next month or more, the API will remain stable with no breaking changes, allowing integrators to confidently implement support. This freeze applies to v0.1 while development continues on v0. We'll use this period to validate the API in real-world integrations and gather feedback to shape v1 for general availability. Thank you to everyone for your contributions and patienceβyour involvement has been key to getting us here!
2025-09-08 update: The registry has launched in preview π (announcement blog post). While the system is now more stable, this is still a preview release and breaking changes or data resets may occur. A general availability (GA) release will follow later. We'd love your feedback in GitHub discussions or in the #registry-dev Discord (joining details here).
Registry Working Group:
Radoslav (Rado) Dimitrov (Stacklok) @rdimitrov - WG Lead
Tadas Antanavicius (PulseMCP) @tadasant
Bob Dickinson (TeamSpark) @BobDickinson
Preeti (Pree) Dewani (Ravenmail) @pree-dew
Related MCP server: io.github.pmgarg/cgraphy
Contributing
We use multiple channels for collaboration - see modelcontextprotocol.io/community/communication.
Often (but not always) ideas flow through this pipeline:
Discord - Real-time community discussions
Discussions - Propose and discuss product/technical requirements
Issues - Track well-scoped technical work
Pull Requests - Contribute work towards issues
Quick start:
Pre-requisites
Docker
Go - version per the
godirective in go.mod; the Go toolchain fetches it for youko - Container image builder for Go (installation instructions)
golangci-lint - version per the
golangci-lint-actionpin in ci.yml, so local lint matches CI
Running the server
# Start full development environment
make dev-composeThis starts the registry at localhost:8080 with PostgreSQL. The database uses ephemeral storage and is reset each time you restart the containers, ensuring a clean state for development and testing.
Note: The registry uses ko to build container images. The make dev-compose command automatically builds the registry image with ko and loads it into your local Docker daemon before starting the services.
By default, the registry seeds from the production API with a filtered subset of servers (to keep startup fast). This ensures your local environment mirrors production behavior and all seed data passes validation. For offline development you can seed from a file without validation with MCP_REGISTRY_SEED_FROM=data/seed.json MCP_REGISTRY_ENABLE_REGISTRY_VALIDATION=false make dev-compose.
The setup can be configured with environment variables in docker-compose.yml - see .env.example for a reference.
Pre-built Docker images are automatically published to GitHub Container Registry. Note that the image does not bundle PostgreSQL, so you need to run your own and point the registry at it via MCP_REGISTRY_DATABASE_URL (see docker-compose.yml for a working example):
# Run latest stable release
docker run -p 8080:8080 ghcr.io/modelcontextprotocol/registry:latest
# Run latest from main branch (continuous deployment)
docker run -p 8080:8080 ghcr.io/modelcontextprotocol/registry:main
# Run specific release version
docker run -p 8080:8080 ghcr.io/modelcontextprotocol/registry:v1.0.0
# Run development build from main branch
docker run -p 8080:8080 ghcr.io/modelcontextprotocol/registry:main-20250906-abc123dAvailable tags:
Releases:
latest,v1.0.0,v1.1.0, etc.Continuous:
main(latest main branch build)Development:
main-<date>-<sha>(specific commit builds)
Publishing a server
Publishing an MCP server does not happen through a pull request to this repository. Adding a server.json, a server directory, or an entry in data/seed.json will not publish or list your server, even if the PR is merged. Use the mcp-publisher CLI from your server's own project instead.
To publish a server, we've built a simple CLI. You can use it with:
# Build the latest CLI
make publisher
# Use it!
./bin/mcp-publisher --helpSee the publisher guide for more details.
Other commands
# Run lint, schema validation, and all tests
# Note: this also runs `make dev-down`, stopping any running dev-compose environment
make checkThere are also a few more helpful commands for development. Run make help to learn more, or look in Makefile.
Architecture
Project Structure
βββ cmd/ # Application entry points
β βββ publisher/ # Server publishing tool
β βββ registry/ # Registry API server
βββ data/ # Seed data
βββ deploy/ # Deployment configuration (Pulumi)
βββ docs/ # Documentation
βββ internal/ # Private application code
β βββ api/ # HTTP handlers and routing
β βββ auth/ # Authentication (GitHub OAuth, JWT, namespace blocking)
β βββ config/ # Configuration management
β βββ database/ # Data persistence (PostgreSQL)
β βββ importer/ # Seed data import
β βββ service/ # Business logic
β βββ telemetry/ # Metrics and monitoring
β βββ validators/ # Input validation
βββ pkg/ # Public packages
β βββ api/ # API types and structures
β β βββ v0/ # Version 0 API types
β βββ model/ # Data models for server.json
βββ scripts/ # Development and testing scripts
βββ tests/ # Integration tests
βββ tools/ # CLI tools and utilities
βββ validate-*.sh # Schema validation toolsAuthentication
Publishing supports multiple authentication methods:
GitHub OAuth - For publishing by logging into GitHub
GitHub OIDC - For publishing from GitHub Actions
DNS verification - For proving ownership of a domain and its subdomains
HTTP verification - For proving ownership of a domain
The registry validates namespace ownership when publishing. E.g. to publish...:
io.github.domdomegg/my-cool-mcpyou must login to GitHub asdomdomegg, or be in a GitHub Action on domdomegg's reposme.adamjones/my-cool-mcpyou must prove ownership ofadamjones.mevia DNS or HTTP challenge
Community Projects
Check out community projects to explore notable registry-related work created by the community.
More documentation
See the documentation for more details if your question has not been answered here!
Available Tools
10 toolsfind_symbolsFind symbolsC
Find source-backed symbols by name or qualified-name pattern. Returns IDs and spans, never arbitrary files.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | ||
| limit | No | ||
| pattern | Yes | ||
| profile | No | ||
| repo_id | Yes | ||
| max_tokens | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, but it does add real behavioral context: results are "source-backed" and return "IDs and spans, never arbitrary files," which tells the agent the result shape and constraints. It omits read-only nature, pagination, and any failure/empty-result behavior for a 6-param 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?
Two tight sentences, front-loaded with the core action and followed by the return-value constraint. No wasted words, though the extreme brevity is part of why parameter detail is missing.
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 6-parameter tool with no annotations, no output schema, and 0% schema description coverage, the definition is under-specified. It states return contents but leaves parameter meaning and usage boundaries to inference.
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 0%, so the description must compensate β but it only hints at the pattern parameter. kind, profile, limit, max_tokens, and repo_id are undocumented anywhere, leaving half the parameter surface opaque (e.g., what kind values are valid, what profile selects).
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?
"Find source-backed symbols by name or qualified-name pattern" gives a specific verb (find) and resource (symbols) plus the lookup key. It clearly distinguishes a symbol search from file-level siblings like search_source, though it never names an alternative explicitly.
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 when the tool is useful (name/qualified-name lookup) but offers no explicit when-to-use, when-not-to-use, or routing guidance among the many symbol-related siblings (inspect_symbol, get_symbol_context, search_source). An agent must infer the boundary itself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_file_skeletonFile skeletonC
Imports and source-backed headers from one file. Function bodies elided by default.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| profile | No | ||
| repo_id | Yes | ||
| max_tokens | No | ||
| include_private | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It usefully discloses that function bodies are elided by default, which is a meaningful behavioral trait, but says nothing about permissions, whether the operation is read-only, truncation behavior from max_tokens, or what the profile parameter alters.
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?
Two short sentences with no filler, and the core output (imports and headers) is front-loaded. The phrasing 'Imports and source-backed headers' is slightly awkward but compact.
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 5-parameter tool with no annotations, no output schema, and 0% schema description coverage, this description is far too thin. It omits parameter meanings, privacy behavior, truncation behavior, and any sense of output shape beyond the elision default.
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 0% across 5 parameters, so the description must compensate but largely does not. 'One file' weakly implies path/repo_id, and 'by default' weakly hints at profile or include_private configurability, but max_tokens, include_private, and the profile semantics remain entirely undocumented.
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 states the specific resource (one file) and what is returned (imports and source-backed headers), and distinguishes the resource from siblings like search_source or inspect_symbol by naming the skeleton concept. It is clear but does not explicitly differentiate itself from the sibling set.
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?
There is no guidance on when to use this tool versus alternatives such as inspect_symbol, get_symbol_context, or search_source. The agent must infer that a file skeleton is preferred over symbol-level or source-text tools, which is not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_impact_sliceImpact candidate sliceC
Traverse observed caller/callee edges from a symbol. Candidate impact slice, not complete blast radius.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | ||
| profile | No | ||
| repo_id | Yes | ||
| direction | No | both | |
| max_nodes | No | ||
| symbol_id | Yes | ||
| max_tokens | No | ||
| min_confidence | No | ||
| filter_ambiguous | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral burden. It usefully discloses that edges are 'observed' and the result is a 'candidate impact slice, not complete blast radius,' but omits permissions, return behavior, and limits for its 9 parameters.
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?
Two short, front-loaded sentences with no wasted words. The scope caveat is placed immediately after the core action, making the description easy to scan.
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 tool with 9 parameters, no output schema, and no annotations, the description is too thin. It gives the core purpose and a scope caveat but leaves parameter behavior and invocation guidance almost entirely undocumented.
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 0% for 9 parameters, and the description adds no meaning beyond 'from a symbol' and 'caller/callee edges.' It does not explain depth, profile, max_nodes, max_tokens, min_confidence, or filter_ambiguous.
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?
States a specific action: traverse observed caller/callee edges from a symbol. The added caveat 'Candidate impact slice, not complete blast radius' clarifies scope, though it does not name sibling alternatives like get_module_dependents.
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?
No explicit when-to-use guidance or alternatives are provided. The caveat 'not complete blast radius' hints at a limitation but does not route the agent to another tool or condition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_index_statusIndex statusB
Active snapshot metadata and paths changed since indexing. Run before relying on graph results.
| Name | Required | Description | Default |
|---|---|---|---|
| repo_id | Yes |
TDQS
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 timing guidance and that it reports index freshness/changed paths, but it does not explicitly state read-only safety, side effects, permissions, or freshness semantics, leaving behavioral traits partly implicit.
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?
Two short sentences with no filler and usage guidance front-loaded after the return summary. The first sentence is a fragment, but the description avoids bloat.
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 no annotations, no output schema, and no parameter descriptions, the description is under-informative: it covers return content at a high level but omits repo_id semantics and safety/profile details. It is not complete enough for an agent to call without additional assumptions.
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 coverage is 0%, so the description must define repo_id, but it never mentions the parameter. The single required parameter's meaning, format, and scope are left to inference from its name alone.
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 supplies the data returned (active snapshot metadata, changed paths since indexing) but never uses a clear verb and does not differentiate this status check from the graph-query siblings. An agent can infer it is a read, but the purpose is not stated as explicitly as a 'returns X' statement would be.
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?
It gives a clear trigger: run before relying on graph results. No when-not condition or alternative tool is named, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_module_dependentsModule dependentsC
Lexical import relationships for indexed path or module. Dynamic imports flagged. Not semantic resolution.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | ||
| module | No | ||
| profile | No | ||
| repo_id | Yes | ||
| max_tokens | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does disclose meaningful caveats: results are lexical rather than semantic, and dynamic imports are flagged separately. However, it never clarifies directionality (do dependents mean files that import this module, or its imports?), nor result format, limits, or indexing requirements.
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?
Three short fragments, front-loaded with the core purpose and the caveat, with no filler. It is telegraphic to the point of strain but every clause carries 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?
For a 5-parameter tool with no schema descriptions, no annotations, and no output schema, the description leaves too much unaddressed: required repo_id, profile, max_tokens, and the direction of the relationship. An agent could not invoke this reliably without guessing.
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 0% across 5 parameters and the description only addresses two of them (path, module). repo_id is required yet unexplained, and profile and max_tokens are never mentioned, so the description does not compensate for the schema gap.
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?
States a specific resource (lexical import relationships) and scopes it to an indexed path or module, which is enough to tell it apart from siblings like get_repo_map or find_symbols. The verb is implicit ('get' in the name), but the returned concept is clearly identified.
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?
There is no when-to-use guidance, no prerequisites, and no reference to sibling alternatives such as get_impact_slice or get_symbol_context. The phrase 'for indexed path or module' hints at applicability but does not guide tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_repo_mapRepository mapB
Ranked definitions for a repo_id within budget. Compact entries are [id, path:line, kind/name]; format='full' adds signatures and evidence. For orientation, not proof of full coverage.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | ||
| format | No | ||
| profile | No | ||
| repo_id | Yes | ||
| budget_tokens | No | ||
| include_tests | No | ||
| include_omitted_ids | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It usefully discloses the entry format, the budget constraint ('within budget'), and a coverage caveat, but says nothing about auth, performance, or how budget truncation interacts with include_omitted_ids.
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?
Three dense sentences, front-loaded with the core purpose and output contract, with the caveat last. No filler.
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 7-parameter tool with no annotations and no output schema, leaving four parameters unexplained and omitting any routing guidance to the many sibling discovery tools is a substantial gap, even though the format and coverage caveat are handled well.
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 coverage is 0% with 7 parameters. Only format='full' vs compact is explained and budget_tokens is hinted at via 'within budget'. query, profile, include_tests, and include_omitted_ids are entirely undocumented in both schema and description.
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?
States a concrete verb+resource ('Ranked definitions for a repo_id') and the two output shapes, which distinguishes it from siblings like get_file_skeleton and get_symbol_context. It does not explicitly name the sibling it complements, so it stops short of 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?
'For orientation, not proof of full coverage' implies when the tool is appropriate, but there is no explicit when-to-use versus alternatives such as get_file_skeleton or inspect_symbol. Usage is suggested rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_symbol_contextSymbol contextC
Bounded source packet for indexed symbol and observed edges. Check freshness and ambiguity warnings. A body larger than max_tokens is read in windows with body_offset_line.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | ||
| profile | No | ||
| repo_id | Yes | ||
| symbol_id | Yes | ||
| max_tokens | No | ||
| include_body | No | ||
| body_offset_line | No | ||
| include_omitted_ids | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It usefully discloses that the result is bounded and that large bodies are read in windows via max_tokens and body_offset_line, but it omits permissions, error behavior, and freshness/ambiguity warning semantics.
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 short and front-loads the core purpose, then adds two relevant behavioral notes. It avoids repetition, though the wording is terse enough that some important details feel compressed rather than clarified.
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 an 8-parameter tool with no annotations, no output schema, and 0% schema description coverage, the description is materially incomplete. It covers the surface purpose and windowing behavior but leaves most parameters, return structure, and alternative-tool selection unexplained.
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 0% and there are 8 parameters, so the description must compensate. It explains only max_tokens and body_offset_line indirectly; depth, profile, include_body, include_omitted_ids, repo_id, and symbol_id remain undocumented beyond their titles.
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 identifies a specific resource: a bounded source packet for an indexed symbol plus observed edges. It communicates what the tool returns, but it does not distinguish the tool from siblings such as inspect_symbol or get_file_skeleton, so an agent must infer the boundary.
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?
It gives output-related advice ('Check freshness and ambiguity warnings') but does not state when to choose this tool over alternatives like inspect_symbol or find_symbols. No prerequisites, exclusions, or selection criteria are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inspect_symbolInspect symbol (composite)C
Resolve a symbol and its immediate graph in one call. view='full' returns a context packet: body, callee/caller signatures, related tests. Use before editing a symbol.
| Name | Required | Description | Default |
|---|---|---|---|
| view | No | normal | |
| query | Yes | ||
| repo_id | Yes | ||
| budget_tokens | No |
TDQS
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 doesn't state whether this is read-only, whether there are rate limits or caching behavior, what 'immediate graph' means in practice, or how the view modes affect behavior. The token budget implication is only hinted at.
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?
Three tight sentences, front-loaded with purpose. No wasted words, though the 'view=full' detail would be better placed after establishing the baseline behavior.
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 4-param composite tool with no annotations and no output schema, the description is thin. It doesn't explain the return packet shape beyond a partial list, doesn't document the budget_tokens interaction, and leaves view semantics mostly opaque.
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 coverage is 0%, so the description must compensate for undocumented params. It mentions view='full' but doesn't explain what 'minimal' or 'normal' return, doesn't clarify the required 'query' semantics (symbol name? pattern?), and never mentions repo_id or budget_tokens.
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?
States a specific verb+resource: 'Resolve a symbol and its immediate graph in one call.' Clear on what the tool does, though it doesn't distinguish itself from sibling get_symbol_context, which likely overlaps significantly.
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?
Says 'Use before editing a symbol,' which implies a workflow context. But it gives no explicit when-not-to-use guidance and doesn't differentiate from close siblings like get_symbol_context or find_symbols.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_repositoriesRegistered repositoriesA
List registered repository IDs. Call this first; roots are never exposed.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It adds one useful behavioral fact ('roots are never exposed'), which tells the agent something about result scoping. However it omits return volume, ordering, and whether an index must exist, leaving the safety/behavioral profile thin for a tool with zero annotation coverage.
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?
Two short sentences, purpose front-loaded, followed by the usage cue and the scoping note. Every clause earns its place with no filler.
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?
A zero-param read tool with no output schema is largely self-describing; the description names what is returned (IDs) and a scope caveat. It could have mentioned whether an index must be built first, but nothing essential to invoking it is missing.
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 takes zero parameters, so per the rubric the baseline is 4. There is nothing for the description to clarify beyond confirming it is a no-argument listing call.
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?
States a specific verb+resource ('List registered repository IDs') and even specifies the return granularity (IDs, not full repo objects). It doesn't explicitly differentiate from siblings like get_repo_map or get_index_status, but the resource is distinct enough that confusion is unlikely.
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?
'Call this first' gives a clear ordering cue for the workflow, which is meaningful for a discovery tool. It does not name an alternative or a when-not condition, so it falls short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_sourceSearch sourceB
Full-text search ranked by enclosing symbol. expand='graph' (default for profile 'locate') adds resolved callers/callees as neighbors. Call before reading files.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| expand | No | auto | |
| profile | No | ||
| repo_id | Yes | ||
| expand_k | No | ||
| max_tokens | No | ||
| expand_hops | No | ||
| min_confidence | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does disclose two real behaviors: symbol-based ranking and graph expansion that adds resolved callers/callees as neighbors. It says nothing about permissions, cost/latency, ranking fallback, or what happens with the non-enum parameters, so transparency is partial.
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?
Three short sentences, front-loaded with the core purpose, then the expand behavior, then a usage cue. No padding, though the parameter detail sentence is dense and cryptic without surrounding context.
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?
A 9-parameter tool with no annotations, no output schema, and 0% schema description coverage needs considerably more from the description. It explains only one parameter interaction and omits the meaning, defaults, and effects of most arguments, leaving an agent under-equipped to invoke it correctly.
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 0% and there are 9 parameters, so the description must compensate. It only clarifies expand='graph' and its interaction with profile 'locate', leaving limit, max_tokens, expand_k, expand_hops, and min_confidence completely undocumented in both schema and description.
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?
States a specific verb+resource ("Full-text search") and adds a distinguishing behavioral detail: results are ranked by enclosing symbol. It does not, however, differentiate itself from siblings like find_symbols or inspect_symbol, so an agent cannot tell from the text alone which search tool to pick.
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?
"Call before reading files" gives an implied usage context, suggesting this is a discovery/entry-point tool. But there is no when-not guidance and no explicit routing to alternatives such as find_symbols for symbol lookups, so the guidance stays at implication level.
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.
1 tool update
v0.3.3- Changed
get_symbol_context1 field changed- added
Input schema / properties / body_offset_lineAdded value: +{ + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Body Offset Line" +}
3 tool updates
v0.2.0- Changed
get_impact_slice2 fields changed- added
Input schema / properties / filter_ambiguousAdded value: +{ + "default": true, + "title": "Filter Ambiguous", + "type": "boolean" +} - added
Input schema / properties / min_confidenceAdded value: +{ + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Min Confidence" +}
- Added
inspect_symbol - Changed
search_source4 fields changed- added
Input schema / properties / expandAdded value: +{ + "default": "auto", + "enum": [ + "auto", + "none", + "graph" + ], + "title": "Expand", + "type": "string" +} - added
Input schema / properties / expand_hopsAdded value: +{ + "default": 1, + "title": "Expand Hops", + "type": "integer" +} - added
Input schema / properties / expand_kAdded value: +{ + "default": 3, + "title": "Expand K", + "type": "integer" +} - added
Input schema / properties / min_confidenceAdded value: +{ + "default": 0.6, + "title": "Min Confidence", + "type": "number" +}
9 tool updates
v0.1.0- First observed
find_symbols - First observed
get_file_skeleton - First observed
get_impact_slice - First observed
get_index_status - First observed
get_module_dependents - First observed
get_repo_map - First observed
get_symbol_context - First observed
list_repositories - First observed
search_source
TDQS
Scored across 10 tools
Most tools target distinct phases (list repos, index status, search, orient, read context, traverse graph), but boundaries blur in places: get_symbol_context and inspect_symbol both return source packets plus edges for a symbol, and get_impact_slice, get_module_dependents, and search_source(expand='graph') all traverse relationships. Descriptions attempt to differentiate via 'one call', 'lexical vs semantic', and 'candidate vs complete', but an agent could still misselect among them.
Every tool follows a consistent verb_noun snake_case pattern (get_*, find_*, list_*, search_*, inspect_*). Prefixes map cleanly to intent: get for retrieval, find/list/search for discovery. No mixed conventions.
Ten tools is well-scoped for a code-context/index server. Each tool earns its place across discovery, search, orientation, symbol reading, and graph traversal, with no redundant filler.
Coverage spans repository listing, index freshness, symbol search, orientation maps, file skeletons, bounded source context, and impact traversal β a solid read-only surface with no obvious dead ends. Minor gaps like direct line-range/file-read or cross-repo comparison are workable around via existing tools.
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