CB Insights MCP Server
OfficialServer Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or confusion between tools. The tool has a single, clear purpose: interacting with the ChatCBI system.
Naming Consistency5/5A single tool inherently has perfect naming consistency. There are no other tools to compare against, so no inconsistency can exist.
Tool Count2/5One tool is too few for a server with a broad purpose like 'CB Insights MCP Server', which suggests access to a data/analytics platform. A single chat interface tool feels thin and incomplete for this domain scope.
Completeness2/5The server appears to provide access to CB Insights data/analytics, but only offers a single chat interface tool. This is severely incomplete—missing core operations like data retrieval, report generation, search, or specific query endpoints that would be expected for such a platform.
Average 2.8/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed 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
- CI status not available
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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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true and openWorldHint=true, indicating safe read operations with open-ended capabilities. The description adds valuable context about chat continuation (multi-turn conversations) and query specificity requirements, which goes beyond what annotations provide. No contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two focused sentences. The first sentence provides usage advice, the second explains chat continuation. No wasted words, though it could be more front-loaded with the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a chat tool with 2 parameters, 0% schema coverage, no output schema, and no sibling tools, the description is insufficient. It doesn't explain what the tool does, what 'CBI' refers to, expected response format, or error conditions. The annotations help but don't compensate for the description's gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate but fails to explain parameter meanings. It mentions 'chat_id' for continuing conversations, giving some context for that parameter, but doesn't explain what 'message' represents or its format. The description adds minimal value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5Does the description clearly state what the tool does and how it differs from similar tools?
The description fails to state what the tool actually does - it only provides usage advice ('provide clear, specific queries') and mentions chat continuation capability. The name 'ChatCBI' suggests a conversational interface, but the description doesn't explicitly state this is a chat/query tool. It's tautological in that it mentions 'using this tool' without explaining its function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage guidance about query specificity and chat continuation, but doesn't explain when to use this tool versus alternatives (though there are no sibling tools). It mentions including chat_id for continuing conversations, which gives context about multi-turn interactions, but lacks explicit when/when-not guidance.
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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