mcp-tool-builder
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation3/5
The tools have distinct purposes (tool creation, Bitcoin price, weather forecast, tool listing), but the domain is unclear—'create_tool' and 'list_available_tools' suggest a tool management server, while the other two are unrelated utilities. This mismatch could confuse agents about the server's scope, though individual tools are not directly overlapping.
Naming Consistency4/5Three tools follow a consistent verb_noun pattern (create_tool, get_bitcoin_price, get_weather_forecast), but 'list_available_tools' uses 'list' instead of 'get', breaking full consistency. Overall, naming is mostly predictable and readable with only minor deviation.
Tool Count3/5With 4 tools, the count is reasonable for a small utility set, but it feels thin for a 'tool-builder' server that might imply more comprehensive tool management. The inclusion of unrelated utilities (Bitcoin, weather) makes the scope ambiguous, affecting appropriateness.
Completeness2/5If the domain is tool management, there are significant gaps: no update, delete, or test tools, and the unrelated utilities don't fit. If it's a general utility server, coverage is sparse and random. In either case, the surface is incomplete for coherent agent workflows.
Average 3.1/5 across 4 of 4 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
- CI status not available
This repository is licensed under The Unlicense.
This repository includes a README.md file.
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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 the tool creates something but doesn't specify whether this requires special permissions, how the tool is integrated (e.g., saved persistently or temporarily), potential side effects, or error handling. This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is front-loaded and appropriately sized for its purpose, making it easy to parse quickly.
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?
Given the complexity of creating a tool (a mutation operation) with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after creation (e.g., success response, tool availability, or error cases), leaving the agent with insufficient context for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters (tool_name, description, code). The description adds no additional meaning beyond implying these parameters define the tool's functionality, which is already clear from the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create') and resource ('a new Python tool'), specifying the functionality aspect. It distinguishes from sibling tools like get_bitcoin_price or get_weather_forecast by focusing on tool creation rather than data retrieval, though it doesn't explicitly differentiate from list_available_tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description lacks context about prerequisites, such as whether this is for extending the server's capabilities or for user-defined tools, and doesn't mention any exclusions or complementary tools.
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 mentions the data source ('NWS API'), which hints at external dependencies and potential rate limits or availability issues, but doesn't explicitly state these. It doesn't describe error handling, response format, or whether the operation is idempotent or has side effects, leaving significant gaps for a tool that interacts with an external API.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the core functionality without unnecessary words. It is front-loaded with the main action and resource, and every part of the sentence adds value (e.g., specifying the API source). There is no redundancy or fluff.
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?
Given the lack of annotations and output schema, the description is incomplete for a tool that interacts with an external API. It doesn't explain what the forecast includes (e.g., temperature, precipitation), how the data is returned, or any error scenarios. For a tool with no structured support, more detail is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description specifies that the forecast is for a 'ZIP code', which clarifies the meaning of the zip_code parameter beyond the schema's generic string type. However, with 0% schema description coverage and only one parameter, the description adds some value but doesn't fully compensate for the lack of schema details (e.g., format constraints or validation rules). The baseline is 3 since the schema coverage is low but the parameter count is minimal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Retrieves weather forecast') and the resource ('for a given ZIP code'), with the specific data source ('using NWS API') adding useful context. It distinguishes itself from sibling tools like get_bitcoin_price by focusing on weather data rather than financial data. However, it doesn't explicitly differentiate from other potential weather-related tools that might exist in a broader context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 any prerequisites, limitations, or suggest other tools for related tasks (e.g., historical weather data or different location formats). The agent must infer usage solely from the tool name and description without explicit context.
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 but only states what the tool does without disclosing behavioral traits like whether it's read-only, requires authentication, has rate limits, or what format the output takes. It's minimal and lacks essential context for a tool that lists resources.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, front-loading the core purpose. It's appropriately sized for a simple tool and earns its place by clearly stating the action.
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?
Given the tool's simplicity (0 parameters, no output schema), the description is incomplete. It lacks context on output format, behavioral aspects like safety or permissions, and doesn't leverage the opportunity to guide usage relative to siblings, making it inadequate for full agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and its mention of 'all currently available tools' implies no filtering, which aligns with the empty schema. Baseline is 4 for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('all currently available tools'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'create_tool' which has a different purpose, so it falls short of 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 Guidelines2/5Does 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 'create_tool' for creating tools or other unrelated siblings. There's no mention of context, prerequisites, or exclusions, leaving usage entirely implicit.
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. It mentions the data source (CoinGecko API) but doesn't describe potential rate limits, error conditions, response format, or whether this is a real-time or cached price. For a tool that fetches external data, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the essential information without any wasted words. It's appropriately sized for a simple tool with no parameters and gets straight to the point about what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and no output schema, the description covers the basic purpose adequately. However, without annotations and with external API dependencies, it should ideally mention response format, potential limitations, or error handling to be more complete for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, maintaining focus on the tool's core functionality without unnecessary detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Gets'), resource ('current Bitcoin price'), and source ('from CoinGecko API'), with the currency unit ('in USD') specified. It distinguishes itself from sibling tools like get_weather_forecast by focusing on cryptocurrency pricing rather than weather data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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, nor does it mention any prerequisites or contextual constraints. It simply states what the tool does without indicating appropriate use cases or limitations.
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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