code-graph-mcp

Server Quality Checklist

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Latest release: v0.3.1
Disambiguation5/5
Each tool has a clearly distinct purpose: finding relations, impact analysis, frontend-to-backend mapping, reindexing, repo summary, symbol search, and visualization. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., find_component_relations, impact_of_change, map_frontend_to_backend). The naming is predictable and intuitive.
Tool Count5/5
With 7 tools, the set is well-scoped for a code graph analysis tool. Each tool serves a necessary role without redundancy or bloat.
Completeness4/5
The tools cover key operations: search, retrieval, impact analysis, mapping, visualization, and reindexing. A minor gap is direct access to file content or diff capabilities, but the set is comprehensive for its stated purpose.
Average 3.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
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 describes fuzzy search and lists indexed node types, implying a read-only, non-destructive operation. However, it lacks details about result format, pagination, or performance characteristics, which would enhance transparency.
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 extremely concise: two sentences with no redundant information. Every word adds value, and the key information is front-loaded.
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?
Given the presence of sibling tools like find_component_relations and impact_of_change, the description sets appropriate context for a search tool. However, it lacks details about output format, sorting, or behavior when the query is empty. For a search tool with four parameters and no output schema, more completeness would be beneficial.
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 only 25%, meaning most parameters lack descriptions. The tool description does not explain how to use 'types', 'limit', or 'max_tokens'. For example, it does not clarify that 'max_tokens' sets a response token budget. This leaves the agent underinformed about parameter usage.
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 clearly states the tool performs fuzzy search over indexed nodes (components, routes, etc.), and distinguishes itself from grep/find by explicitly stating 'Use instead of grep/find for 'where is X defined?'. This provides a specific verb-resource combination and differentiates from alternative 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 explicitly suggests using this tool instead of grep/find for finding definitions. It implies a clear usage context. While it doesn't list exclusions or mention sibling tools, the context is adequate and the provided alternative is concrete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses transitive behavior up to depth N, but omits performance implications, auth needs, or whether results are real-time. Adequate but not thorough.
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, front-loaded with key action, no wasted words. Efficiently conveys purpose and usage.
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?
No output schema, so description should explain return format. It says 'list everything', but doesn't specify structure (e.g., list of nodes, paths, depth levels). Missing details on max_tokens truncation behavior. Adequate but incomplete for agent invocation.
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 67%, so baseline 3. Description adds context to 'depth' parameter via 'up to depth N', but doesn't add significant meaning beyond schema descriptions for 'name' and 'max_tokens'.
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?
Description clearly states verb 'list' and resource 'dependencies', specifies input types (component, handler, endpoint, file path), and distinguishes from siblings like find_component_relations and map_frontend_to_backend.
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?
Explicitly says 'Use before refactors and when debugging what breaks if I change this?', providing clear context for when to use, though no exclusions or alternatives mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 inputs and directionality but does not disclose details like matching exactness, handling of multiple matches, performance implications, or whether it requires network access.
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 wasted words. First sentence states the action, second gives examples and use case. Highly structured and front-loaded.
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 covers purpose, inputs, and use case well. However, it does not mention what the output looks like (e.g., a list of chain steps), which is important for an agent to interpret results. Given no output schema, this gap affects completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions, but the tool description adds examples ('LoginPage', '/login', '/api/login') and clarifies that it works from either end, providing context beyond the schema.
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 clearly states the tool traces the full frontend-to-backend chain, specifying inputs like component name, route, or endpoint. It uses a specific verb ('trace') and resource ('full chain'), distinguishing it from siblings.
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 explicitly says 'Use for where does X land in the backend?' and provides examples of acceptable inputs. It implies the context of use but does not explicitly state when not to use or name alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry burden. It explains the two modes (incremental and full) but does not disclose whether the operation is safe, reversible, or has side effects beyond updating the graph.
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 wasted words. The key action and parameter usage are front-loaded, making it easy to parse.
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 the tool's simplicity (one optional parameter, no output schema), the description covers the purpose and usage adequately. Could mention return value or effect on state, but not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has one boolean parameter with no description (0% coverage). The description adds meaning by specifying that `full:true` triggers a full rebuild, which compensates well.
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 it refreshes the graph and explains incremental vs full rebuild. It distinguishes from sibling tools that focus on relations or analysis, though could be more explicit about the graph's 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?
Explicitly states when to call ('after significant code edits') and how to use the `full` parameter. No mention of when not to use, but the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It lists what the tool returns but does not mention non-destructive behavior, error handling, or any side effects. The description implies a read-only operation but does not confirm auth needs or rate limits.
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 two sentences with no fluff. The first sentence front-loads the purpose and expected output. The second sentence provides a usage guideline. Every sentence earns its place.
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 the complexity (3 params, no output schema), the description provides a comprehensive list of relationship types. However, it does not describe the return format (e.g., JSON structure) or how results are structured. With no output schema, a more detailed explanation of the output would improve completeness.
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 100%, so baseline is 3. The description adds value for the 'name' parameter by explaining it includes component or route names with examples. For 'detail' and 'max_tokens', it adds little beyond the schema; 'max_tokens' simply says 'response token budget' which is already in the schema.
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 clearly states the tool's purpose: given a component or route name, return related elements. It uses specific verbs like 'return' and lists concrete outputs (children, parents, hooks/stores, endpoints, imports). It also distinguishes itself from the common alternative 'grepping for usages'.
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 guidance on when to use: when you need to find relations of a component or route. It also explicitly suggests using this tool instead of grepping. However, it does not compare to sibling tools like 'search_symbols' or 'impact_of_change', nor does it state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. Mentions 'session token-savings stats' hinting at cost tracking, but does not disclose caching, auth needs, or side effects. Adequate but not thorough.
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 concise sentences. Front-loaded with main purpose, no wasted words.
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?
Covers what the tool returns (packages, routes, stats) and notes token-savings. No output schema, but description is sufficient for a summary tool. Could mention it returns text.
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 100%, so baseline is 3. Description adds no extra parameter-specific guidance beyond what schema already provides.
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 clearly states 'high-level overview' and lists specific content (packages, routes, stats), distinguishing it from directory listing. The verb 'Call' implies retrieval.
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?
Explicitly says 'Call this FIRST for any structural question instead of listing directories,' providing clear context. Could be improved by comparing to siblings like find_component_relations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits fully. It describes the return format and the two kinds, but does not disclose any side effects, authorization needs, or rate limits. The read-only nature is implied but not explicitly stated.
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 two sentences, front-loaded with the core purpose and parameters, followed by a practical usage tip. Every sentence earns its place with no redundancy.
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?
With 5 parameters, no output schema, and no annotations, the description should provide more depth. It explains the output format and kinds but omits details on 'max_nodes' behavior and the exact nature of the returned diagram. The practical tip helps, but there is room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 40%, but the description adds meaning for 'kind' (explains the two options) and 'format' (states default). It does not add info for 'max_nodes' or 'max_tokens' beyond the schema. The added value for key parameters warrants a score above the baseline.
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 clearly states it returns a Mermaid or Graphviz DOT diagram for a graph slice, with explicit kinds 'component-tree' and 'fe-to-be'. This distinguishes it from sibling tools like 'find_component_relations' and 'map_frontend_to_backend' which perform different operations.
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 provides clear context on when to use (for visual graph slices) and a practical tip to paste the Mermaid block into chat. It does not explicitly state when not to use or compare with alternatives, but the sibling list and distinct purpose imply appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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