OSSInsight MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap. get_collection and list_collections handle collections, get_developer_analysis focuses on developers, get_repo_analysis targets repositories, and natural_language_query provides a unique chat interface. The descriptions make it easy to differentiate between these five distinct functions.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case naming. The verbs (get, list, natural_language) are appropriately descriptive for their actions, and the nouns (collection, developer_analysis, repo_analysis, query) clearly indicate the target resources. There are no deviations in naming conventions.
Tool Count5/5
With 5 tools, this server is well-scoped for its purpose of providing GitHub data analysis through OSSInsight. Each tool serves a specific, valuable function without redundancy, covering collections, repositories, developers, and natural language queries. This count is appropriate and manageable for the domain.
Completeness4/5
The tool surface covers the core OSSInsight functionality well, including collection management, repository and developer analysis, and natural language queries. A minor gap exists in the lack of update or delete operations for collections, but given the server's focus on data retrieval and analysis rather than management, this is a reasonable limitation that agents can work around.
Average 2.9/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a read operation ('Get information'), which is helpful, but doesn't describe what information is returned (e.g., metadata, repository list, permissions), error conditions, or any rate limits or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's appropriately sized for a simple lookup tool and front-loads the core purpose immediately.
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?
For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what information is returned about the collection (e.g., name, description, repositories, permissions), which is critical for an agent to understand the tool's utility. The lack of behavioral context and output details leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'collection_id' documented in the schema. The description doesn't add any additional meaning about the parameter beyond what the schema provides (e.g., examples of valid IDs beyond 'open-source-database', format constraints). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get information about') and resource ('a specific collection of repositories'), making the purpose understandable. However, it doesn't distinguish this tool from its sibling 'list_collections', which presumably lists multiple collections rather than retrieving details about a specific one.
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 guidance on when to use this tool versus alternatives. It doesn't mention the sibling 'list_collections' for browsing collections or 'get_repo_analysis' for repository-level details, nor does it specify prerequisites like needing a collection ID first.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'detailed analysis' but doesn't specify what that entails—e.g., whether it includes metrics like commit frequency, pull requests, or language usage. It also lacks information on rate limits, authentication needs, or response format, which are critical for a tool with no output schema.
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, efficient sentence that front-loads the core purpose without unnecessary words. It could be slightly more structured by explicitly listing analysis components, but it avoids redundancy and stays focused, earning a high score for conciseness.
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 the complexity of developer analysis and the absence of both annotations and an output schema, the description is incomplete. It doesn't explain what 'detailed analysis' includes, how results are returned, or any behavioral traits like data freshness or limitations. For a tool with no structured output documentation, this leaves significant gaps for an AI agent.
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?
The input schema has 100% description coverage, with the 'username' parameter clearly documented as 'GitHub username'. The description adds no additional parameter details beyond what the schema provides, such as format constraints or examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get detailed analysis of a GitHub developer, including their activity and contributions.' It specifies the verb ('Get'), resource ('GitHub developer'), and scope ('detailed analysis'). However, it doesn't explicitly differentiate from sibling tools like 'get_repo_analysis' or 'natural_language_query', which prevents a perfect score.
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 guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'get_repo_analysis' for repository-level insights or 'natural_language_query' for broader queries. There's no context about prerequisites or exclusions, leaving usage decisions ambiguous.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions what metrics are included, it doesn't describe important behavioral aspects like whether this is a read-only operation, what permissions might be required, whether there are rate limits, what format the analysis returns, or if there are any side effects. The description is too minimal for a tool that presumably makes API calls to GitHub.
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 appropriately concise with a single sentence that efficiently communicates the core purpose. It's front-loaded with the main action ('Get detailed analysis') and includes relevant details. There's no wasted verbiage or unnecessary elaboration, though it could benefit from slightly more context.
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?
For a tool that presumably makes external API calls to GitHub and returns complex analysis data, the description is incomplete. With no annotations and no output schema, the description should provide more context about what 'detailed analysis' includes, what format it returns, any authentication requirements, rate limits, or error conditions. The current description leaves too many important questions unanswered for effective tool use.
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?
The description adds no specific parameter information beyond what's already in the schema. With 100% schema description coverage, both parameters are well-documented in the schema itself. The description doesn't provide additional context about parameter usage, constraints, or examples that would help an agent understand how to use them effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get detailed analysis') and resources ('GitHub repository'), and lists key metrics included (activity, stars, issues). It distinguishes itself from sibling tools like 'get_collection' or 'list_collections' by focusing on repository analysis rather than collections. However, it doesn't explicitly differentiate from 'get_developer_analysis', which might be a related sibling.
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 guidance on when to use this tool versus alternatives. There's no mention of when this analysis tool is appropriate versus other sibling tools like 'get_developer_analysis' or 'natural_language_query'. It lacks any context about prerequisites, limitations, or typical use cases for repository analysis.
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 the full burden of behavioral disclosure. It states the tool queries data but doesn't describe what happens during execution—e.g., whether it's read-only, requires authentication, has rate limits, or returns structured data. For a query tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and 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?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose and uses clear terminology. Every part of the sentence earns its place by specifying key details like the data source and interface.
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 the complexity of a natural language query tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of results to expect, how queries are processed, or any limitations. For a tool that interacts with an external interface (OSSInsight chat), more context on behavior and output is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'query' fully documented in the schema as a natural language string. The description adds no additional parameter semantics beyond what the schema provides, such as query formatting tips or examples beyond the schema's example. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query GitHub data using natural language through the OSSInsight chat interface.' It specifies the action (query), resource (GitHub data), and method (natural language via OSSInsight). However, it doesn't explicitly differentiate from sibling tools like get_developer_analysis or get_repo_analysis, which might also query GitHub data but with different approaches.
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 guidance on when to use this tool versus alternatives. It mentions the OSSInsight chat interface but doesn't explain when natural language queries are preferred over structured queries or how this differs from sibling tools like get_collection or list_collections. There are no explicit when/when-not statements or named alternatives.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'List all available repository collections' implies a read-only operation, it doesn't mention pagination behavior (implied by parameters), rate limits, authentication requirements, or what format the results will be in. For a tool with no annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states exactly what the tool does with zero wasted words. It's appropriately sized for a simple list operation and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with 2 documented parameters and no output schema, the description is minimally adequate but has clear gaps. It doesn't explain the return format, pagination behavior, or how this tool differs from siblings. Without annotations, the description should provide more behavioral context about what 'listing' entails.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter semantics beyond what's in the schema - it doesn't explain how pagination works, default values, or constraints. Baseline 3 is appropriate when the schema does all the parameter documentation work.
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 ('List') and resource ('repository collections'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'get_collection' which presumably retrieves a specific collection rather than listing all available ones.
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 guidance on when to use this tool versus alternatives like 'get_collection' or 'natural_language_query'. There's no mention of prerequisites, appropriate contexts, or limitations that would help an agent choose between available options.
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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