graphify-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: adding sources, building graphs, checking freshness, exploring nodes and paths, analyzing communities, and querying. There is no overlap between tool functionalities.
Naming Consistency4/5
All tools use the 'graphify_' prefix followed by descriptive names. While some are verbs (e.g., graphify_build) and others nouns (e.g., graphify_communities), the pattern is consistent and names clearly indicate tool purpose.
Tool Count5/5
With 16 tools, the server covers the core operations for knowledge graph management—building, adding sources, exploring, and analyzing—without being bloated. Each tool serves a clear need.
Completeness4/5
The tool set covers building, adding, exploring, querying, and community analysis. Missing are tools for deletion or modification of nodes/edges, but the surface is largely complete for the intended exploration and building workflow.
Average 3.5/5 across 16 of 16 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 17 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
Annotations already provide readOnlyHint=true, indicating safe read. The description adds minimal behavioral context beyond the purpose, not expanding on what 'unexpected' means or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Despite being concise (one sentence), it omits essential details about parameters and usage, making it under-specified rather than effectively concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 16 sibling tools and 2 parameters with no description, the description fails to provide sufficient context for an agent to correctly select and invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation of parameters like 'limit' or 'as_json'. This is a critical gap.
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 lists unexpected cross-file/cross-domain connections (surprise edges), specifying the verb 'list' and the unique resource type. However, it does not differentiate from siblings like graphify_neighbors or graphify_communities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It does not mention when not to use it or provide context about typical use cases.
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?
Annotations already indicate readOnlyHint=true, so description adds minimal behavioral context beyond 'with relations'. Does not disclose behavior for missing nodes, format of output, or any side effects. Schema coverage is 0%, so description bears more burden but fails to provide sufficient detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single concise sentence with no wasted words. However, the brevity leaves significant gaps in clarity. It is appropriately front-loaded but would benefit from more detail.
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?
Despite having an output schema (unknown content), the description lacks context for parameters and does not specify what 'relations' means or how results are structured. The tool is simple, but with 0% schema coverage, the description should compensate more.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
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 what 'node' should be (e.g., ID, name) or what 'as_json' does. The agent receives no guidance on parameter values beyond the schema types.
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 lists direct (1-hop) neighbors with relations, using a specific verb and resource. It distinguishes from sibling tools like 'graphify_node_details' or 'graphify_path' by specifying 'neighbors', but lacks explicit comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool over alternatives. Does not mention any context where it is appropriate or inappropriate, nor reference sibling tools for different needs.
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?
Annotations already indicate readOnlyHint=true, so the tool is safe. The description adds 'Return everything Graphify knows', which implies it may be expensive or broad, but no further behavioral traits are disclosed (e.g., what 'everything' includes). Given the annotation covers safety, a score of 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but lacks structure. It front-loads the purpose but omits details that would be helpful. It could be improved by adding a brief note on the parameter and perhaps a usage hint.
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 that an output schema exists, the description does not need to explain return values. However, with multiple sibling tools and a vague 'everything' claim, the description is minimally complete. It tells the agent what the tool does but not enough to differentiate or set expectations fully.
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 coverage is 0%, meaning the parameter 'node' has no description in the schema. The description does not explain what the parameter expects (e.g., node name, ID, format). It only implies it refers to a node. This is insufficient for an agent to correctly invoke the tool.
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 'Return everything Graphify knows about a node' clearly states the action (return) and the resource (everything about a node). It distinguishes from sibling tools like graphify_node_details or graphify_neighbors, which imply more focused queries, but does not explicitly differentiate. The purpose is clear but could be more specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not specify when to use this tool versus other similar tools such as graphify_node_details or graphify_search. There is no guidance on prerequisites, limitations, or 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?
The description adds context about the type of metadata returned (type, source file/line, docstring, community), which is beyond the readOnlyHint annotation. However, it does not disclose error conditions, performance implications, or whether the node must exist in a built graph.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It efficiently conveys the tool's purpose, though it could be slightly expanded with parameter details without becoming verbose.
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 that the schema lacks descriptions and there is no output schema shown, the description should provide more context about usage and return values. It is too brief to fully inform an AI agent about the tool's behavior and prerequisites.
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 does not explain the parameters 'node' or 'as_json' at all. With 0% schema description coverage, the description fails to add meaning beyond the parameter titles and types. For example, it does not clarify expected format of 'node' or impact of 'as_json'.
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 that the tool shows a node's full metadata including type, source file/line, docstring, and community. This is specific and actionable, but it does not differentiate from sibling tools like graphify_neighbors or graphify_overview, which could overlap in purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 its siblings. The description lacks context about prerequisites, when not to use it, or which alternative tools might be more appropriate for related tasks.
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?
Annotations provide readOnlyHint=true, so the agent knows it's safe. The description adds no extra behavioral context beyond that.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short and to the point. It could be slightly more informative but is efficient.
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 simple structure (one boolean param, output schema exists), the description is adequate but lacks guidance on usage and parameter semantics.
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?
With 0% schema description coverage, the description should explain the 'as_json' parameter but does not. The parameter name gives some hint, but no extra meaning is added.
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 'Summarize Leiden communities with sizes and sample members' clearly states the action and output. It distinguishes from siblings like graphify_label_communities, but does not explicitly differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when or when not to use this tool. The description lacks context for selection among siblings.
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?
Annotations already declare readOnlyHint=true. The description adds minimal extra behavioral context beyond 'exact path', without explaining the algorithm or constraints.
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?
Single sentence with example, front-loaded and efficient without wasted words.
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?
Has output schema covering return values, but lacks parameter descriptions. For a simple two-param tool, it is somewhat incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It does not describe parameters node_a and node_b beyond the example, adding no semantic value.
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 finds the exact path between two nodes, with a concrete example. This distinguishes it from siblings like graphify_neighbors or graphify_search.
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 when needing a path between two specific nodes, but lacks explicit guidance on when not to use it or alternatives among siblings.
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?
Annotations already provide readOnlyHint=true, consistent with listing. Description adds the concept of 'god nodes' and highest-degree ordering, but no extra behavioral traits (e.g., pagination, data freshness). Minimal added value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, concise and front-loaded. Efficient but could add slight detail without losing brevity.
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?
Output schema exists, covering return structure. Description explains the core concept. However, with 0% schema coverage and no parameter explanations, an agent may lack complete information for invocation, especially for 'as_json' and 'top_n' semantics.
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%, but description does not explain parameters. 'top_n' and 'as_json' meanings are absent. The description only mentions 'highest-degree' without linking to parameters.
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 uses specific verb 'list' and resource 'god nodes', clearly defining them as 'highest-degree (most connected)' nodes. This distinguishes it from siblings like graphify_neighbors or graphify_node_details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Does not mention context like global scope or preconditions. Among many sibling graph tools, explicit usage notes are lacking.
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?
Annotations already indicate readOnlyHint=true, so description adds little beyond case-insensitivity. It does not disclose the behavior of the 'limit' or 'as_json' parameters, nor any pagination or performance traits. Minimal value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with key action. No wasted words. However, lacks structure detailing different aspects of usage, such as parameter effects.
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?
Output schema exists, so return values are covered. However, important parameters like limit and as_json are not explained in description. For a tool with 3 parameters and sibling tools, more contextual info (e.g., response format) would improve completeness.
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%, so description must compensate. It explains 'pattern' implicitly, but 'limit' and 'as_json' are left undocumented. The description adds meaning for only one of three parameters.
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 'Search nodes by text in their name/label (case-insensitive)'. The verb 'search' and resource 'nodes' are specific, and the case-insensitivity distinguishes it from potential sibling tools that might be case-sensitive. Compared to siblings like graphify_neighbors, graphify_node_details, and graphify_subgraph, this tool's purpose is distinct.
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?
Usage is implied: use when you need to find nodes by text pattern. However, no explicit guidance on when not to use or alternatives. Sibling tools like graphify_neighbors or graphify_node_details could be alternatives but are not mentioned. The description lacks exclusionary context.
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?
Annotations already declare readOnlyHint=true. The description adds that it uses BFS, caps tokens, and supports fuzzy matching, but does not disclose behavior on budget exceedance, missing nodes, or the output format beyond what the output schema provides.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences and a bulleted args list. It is front-loaded and well-structured, though the args section could be integrated more seamlessly.
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 core functionality and parameters but misses details on the 'as_json' parameter and error handling. The presence of an output schema reduces the need to explain return values, but extra context would improve completeness.
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%, so the description must compensate. It explains node, hops, and budget_tokens, but omits the 'as_json' parameter entirely. This incomplete coverage lowers the score.
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 extracts a BFS subgraph around a node with a token budget. This specific verb+resource is distinct from sibling tools, and the phrase 'token-cheap way to hand the model just the relevant slice' further differentiates it.
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 for obtaining a relevant subgraph slice versus the whole graph, but it lacks explicit guidance on when not to use it or alternatives among the 15 siblings.
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?
The description discloses the additive nature and the http/https constraint. Annotations indicate destructiveHint=false, which aligns with adding. However, it does not explain whether the addition is permanent, if duplicates are checked, or what the response 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 extremely concise: two sentences plus a bullet list of arguments. It front-loads the purpose and constraints, with no unnecessary words.
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 tool's simplicity and the presence of an output schema, the description covers the core action. However, it could benefit from mentioning validation behavior or side effects on the graph. It is adequate but not thorough.
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?
With 0% schema description coverage, the description adds meaningful semantics for all three parameters: url is the source URL, author is the original author tag, contributor is the tag for who added it. This compensates for the lack of schema descriptions, though it could provide more format details.
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 verb 'Add' and the resource 'external source to the graph', with concrete examples (arXiv paper, tweet) and a protocol constraint (http/https only). It distinguishes this tool from siblings by its unique function of adding external sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives, nor does it specify prerequisites or exclusions. The usage is implied by the name, but there is no contextual advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true; the description adds context that it is a lightweight, cheap call for orientation, which is consistent and provides additional behavioral insight beyond annotations.
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?
Very concise and front-loaded; first sentence is clear and imperative, and every sentence adds value without unnecessary words.
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 an output schema, the description adequately lists return values, but fails to describe input parameters, making it incomplete for a tool with undocumented parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
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 the two parameters (top_n, as_json) or their meaning, leaving the AI agent without guidance on parameter values despite having defaults.
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?
Clearly states the tool's purpose as a one-shot orientation to call first, listing specific outputs (graph size, top god nodes, community count, etc.) and differentiates from sibling tools by positioning it as the initial cheap exploration call.
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 instructs to call this tool first and describes it as a one-shot orientation for cheap planning, but does not specify when not to use it or mention alternative tools by name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare destructiveHint=false, and the description adds behavioral details: writing to graphify-out/, incremental update behavior, and optional skip of visualization. This goes beyond what annotations alone provide, though it lacks information on auth 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with an Args list, front-loaded with the main purpose. It is slightly verbose but every sentence adds value. Could be tightened slightly but remains efficient.
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 0% schema coverage and 5 parameters, the description covers all key inputs and the primary output (graphify-out/). The presence of an output schema means return values need not be explained. The description is complete enough for correct tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates fully by explaining each parameter: path, mode (deep vs empty), update (re-extract changed files), cluster_only (rerun clustering), and no_viz (skip HTML). This adds significant meaning beyond the bare schema.
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 verb 'Build or update' and the resource 'knowledge graph from a folder', including the output destination. It is specific enough to differentiate from some siblings like graphify_add, though it doesn't explicitly contrast with all siblings.
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 explains parameter modes (deep, update, cluster_only, no_viz) with brief guidance on when to use each. However, it does not provide explicit context on when to use this tool versus alternatives like graphify_add or graphify_query.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the agent knows it's safe. The description adds valuable behavioral details: dfs enables depth tracing, budget caps tokens. This goes beyond the title 'Query graph' in annotations.
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: one sentence for purpose, then parameter docs. No superfluous text. Front-loaded with the main action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (handling return values), the description adequately covers all input parameters and basic behavior. For a 3-param tool with low schema coverage, this is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, placing full burden on the description. It explains all three parameters: question with an example, dfs as depth tracing, budget as token cap. This adds significant meaning beyond the schema's bare types.
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 verb 'Run' and the resource 'natural-language query against the graph,' distinguishing it from sibling tools like graphify_search or graphify_explain which have different query modes. However, it does not explicitly contrast with siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. The description only explains parameters, not usage context or exclusions. For example, it does not say 'use this for natural language queries; use graphify_search for keyword search.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as readOnlyHint=true, and the description adds behavioral context: it uses built_at_commit over mtime, handles modified and untracked files, and flags staleness. This complements the annotations without contradiction.
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 (two sentences), front-loaded with the core purpose, and every sentence adds value. No extraneous information.
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 (1 optional param, 0 required) and the presence of an output schema, the description covers the essential behavior. It explains the staleness check logic and recommends next steps, though it could briefly mention that output includes a boolean result.
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 input schema has one parameter (as_json) with no description (0% schema_description_coverage). The tool description does not mention this parameter or its effect on output, so it provides no added value over 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 checks whether graph.json is stale relative to the current git HEAD. It uses a specific verb ('check') and resource ('graph.json staleness'), and distinguishes itself from sibling tools by recommending graphify_build(update=True) if stale.
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 explains that it prefers built_at_commit over file mtime, and recommends graphify_build(update=True) if stale. This provides clear guidance on when to use and what to do next, though it lacks explicit exclusions or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds detailed behavioral context beyond the readOnlyHint annotation by specifying exactly what status is reported (client support, API key configuration, and impact on graphify_label_communities). No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (three sentences) and front-loaded with 'Capability test'. However, it could be slightly more structured (e.g., listing what is reported). Minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one boolean parameter, readOnly annotation, and output schema), the description sufficiently covers what the tool does and its output meaning, including the relation to graphify_label_communities. It is complete for its purpose.
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?
With schema description coverage at 0%, the description does not explain the single parameter 'as_json'. The parameter name is self-explanatory, but the description adds no value beyond the schema, failing to compensate for the low coverage.
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 reports whether the client supports host-LLM sampling, whether a backend API key is configured, and which method graphify_label_communities will pick. This is a specific verb+resource combination and distinguishes itself from siblings by focusing on sampling/LLM status.
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 says 'Capability test' implying it's used to check sampling capabilities, but it does not explicitly state when to use it vs. alternatives or when not to use it. The usage context is implied but lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-destructive and non-read-only behavior. The description adds value by explaining that host-LLM sampling may be used without an API key, that names are generated with cost implications, and that only the largest communities are named. It does not disclose if the tool modifies the graph permanently or any rate limits, but overall adds useful behavioral context beyond annotations.
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, with a single opening sentence stating purpose, then a clear bullet-style list for each parameter. No unnecessary text or repetition. Every sentence 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 the tool's complexity (4 optional parameters with defaults) and the presence of an output schema, the description covers the main inputs and behavior well. However, it does not mention prerequisites (e.g., need for an existing graph or communities) or the exact side effect of naming (whether it persists or is ephemeral). Still, it is largely complete for typical use.
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?
The description adds significant meaning to three of the four parameters ('method', 'limit', 'sample_size'), explaining their options, defaults, and behavioral effects. However, the 'as_json' parameter is not mentioned at all, leaving its purpose unclear. Since schema coverage is 0%, the description compensates well for most but not all parameters.
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: 'Give the Leiden communities human-readable names.' It uses a specific verb ('give') and resource ('communities'), and the outcome is clear. Among siblings like graphify_communities (which likely lists or computes communities) and graphify_build, this tool uniquely handles naming, providing good differentiation.
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 detailed guidance on the 'method' parameter, explaining when to use 'auto', 'sampling', 'cli', or 'placeholder', including fallback logic and cost considerations for 'limit'. However, it does not explicitly state when to use this tool versus alternatives (e.g., other graphify tools) or 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.

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