Colorado DWR MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes targeting different DWR resources like administrative calls, surface water stations, water rights, and well permits. However, 'get_surface_water_stations' and 'get_surface_water_ts_day' could potentially be confused as both relate to surface water stations, though their actions (search vs. get time series) differ. The 'query_dwr_api' tool is clearly distinct as a generic fallback.
Naming Consistency5/5
All tools follow a consistent 'get_' or 'query_' verb prefix with snake_case naming, making them predictable and readable. The pattern is uniform across all six tools, with no deviations in style or convention.
Tool Count4/5
With 6 tools, the count is reasonable for a DWR data server, covering key resources like water stations, rights, and permits. It's slightly lean but not inadequate, as the generic 'query_dwr_api' tool can extend functionality. A few more specialized tools might enhance coverage, but this is well-scoped.
Completeness3/5
The tools cover read operations for major DWR data types, but there are notable gaps in lifecycle coverage. For example, there are no tools for creating, updating, or deleting records, which might be expected in a full administrative system. The generic query tool helps mitigate this, but the surface is incomplete for CRUD workflows.
Average 2.8/5 across 6 of 6 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 is passing
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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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action ('Get') without detailing whether this is a read-only query, if it requires authentication, what the output format might be (e.g., list, single value), or any rate limits. For a tool with no annotation coverage, this is a significant gap in transparency.
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 with no wasted words, making it appropriately concise. However, it lacks front-loading of critical details (e.g., purpose could be more specific), and the brevity contributes to gaps in other dimensions like guidelines and transparency.
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 tool's complexity (3 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what 'net amounts' entail, how results are returned (e.g., paginated with 'pageSize'), or any behavioral traits. Without annotations or an output schema, the description should provide more context to be fully helpful.
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 clear descriptions for each parameter (e.g., 'Name of the water right', 'Water division number', 'Number of results to return'). The description adds no additional meaning beyond this, such as explaining relationships between parameters or usage examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get net amounts for water rights' states a clear verb ('Get') and resource ('net amounts for water rights'), which establishes the basic purpose. However, it lacks specificity about what 'net amounts' means (e.g., current balance, historical data) and doesn't distinguish this tool from potential siblings like 'query_dwr_api' that might also retrieve water-related data. This makes it vague but not tautological.
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 any prerequisites, context (e.g., for reporting, analysis), or exclusions, and it doesn't reference sibling tools like 'get_surface_water_stations' or 'query_dwr_api' that might handle related queries. This leaves the agent with no usage direction.
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 the action without disclosing behavioral traits. It doesn't mention whether this is a read-only operation, potential rate limits, authentication needs, or what 'active' implies (e.g., real-time vs. cached data), leaving significant gaps in understanding how the tool behaves.
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 with zero waste. It's appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a vague purpose, the description is incomplete. It doesn't clarify what 'administrative calls' are, what data is returned, or how the tool fits into the water management context implied by sibling tools, leaving the agent with insufficient information 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?
The input schema has 100% description coverage, with the single parameter 'division' documented as 'Water division number'. The description adds no additional meaning beyond this, such as explaining what a water division is or how it affects results. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get active administrative calls' states the verb ('Get') and resource ('active administrative calls'), but it's vague about what 'administrative calls' are in this water management context. It doesn't distinguish from sibling tools like 'get_water_rights_net_amount' or 'get_well_permits', leaving ambiguity about the specific domain scope.
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 is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context (e.g., water division relevance), or exclusions, leaving the agent to infer usage from the tool name alone among unrelated siblings like 'get_surface_water_stations'.
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 for behavioral disclosure. It only states 'Search for well permits', which implies a read operation but lacks details on permissions, rate limits, pagination (despite a pageSize parameter), or response format. This is inadequate for a tool with parameters and 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, directly stating the tool's purpose. Every word earns its place, making it highly concise and well-structured.
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 tool has 3 parameters, no annotations, and no output schema, the description is incomplete. It fails to explain behavioral aspects like search behavior, result format, or usage context. For a search tool with multiple inputs, more detail is needed to guide effective use, especially without structured output information.
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 documents all three parameters (wellName, receipt, pageSize) with descriptions. The description adds no additional meaning beyond what the schema provides, such as how parameters interact or search logic. Baseline 3 is appropriate as the schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Search for well permits' clearly states the verb (search) and resource (well permits), providing basic purpose. However, it lacks specificity about scope or differentiation from sibling tools like query_dwr_api, making it somewhat vague. It doesn't restate the name/title tautologically, but could be more detailed.
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 query_dwr_api or other sibling tools. There is no mention of specific contexts, prerequisites, or exclusions. It merely states what the tool does without indicating appropriate usage scenarios.
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 'query any Colorado DWR REST API endpoint,' which implies a read-only operation, but doesn't specify authentication needs, rate limits, error handling, or response formats. For a generic API tool with no annotation coverage, this is a significant gap in 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 a single, efficient sentence that directly states the tool's purpose without unnecessary details. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every word earns its place, avoiding redundancy or fluff.
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 generic API query tool with 2 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how to handle errors, or provide context on API constraints. This makes it inadequate for an agent to use the tool effectively without additional assumptions.
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 clear descriptions for both parameters (endpoint and params). The description adds no additional meaning beyond what the schema provides, such as examples of valid endpoints or common query parameters. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but no extra value is added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as 'Generic tool to query any Colorado DWR REST API endpoint,' which is clear but vague. It specifies the verb 'query' and the resource 'Colorado DWR REST API endpoint,' but lacks specificity about what types of queries or endpoints are supported. It doesn't distinguish from sibling tools like get_surface_water_stations, which suggests overlap in functionality without clear differentiation.
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 any prerequisites, exclusions, or comparisons to sibling tools such as get_surface_water_stations or get_well_permits. This leaves the agent without context for selecting the appropriate tool, relying solely on the generic nature of the description.
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 states it's a search operation, implying read-only behavior, but doesn't mention any constraints like rate limits, authentication needs, or what happens with no results. For a search tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, clearly front-loaded with the core purpose. It's appropriately sized for a search tool with well-documented parameters.
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 (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return format, pagination behavior (implied by pageSize), or error handling. For a search tool with multiple filtering options, more context is needed to guide 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?
The schema description coverage is 100%, so all parameters are documented in the schema. The description adds no additional meaning beyond the schema, such as explaining relationships between parameters (e.g., how division and waterDistrict interact) or search semantics. Baseline 3 is appropriate when the schema handles parameter documentation.
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 action ('Search for') and resource ('surface water stations in Colorado'), providing a specific purpose. However, it doesn't differentiate from sibling tools like 'get_surface_water_ts_day' or 'query_dwr_api', which might also involve surface water data, so it doesn't fully distinguish from alternatives.
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 is provided on when to use this tool versus alternatives such as 'get_surface_water_ts_day' (which likely retrieves time-series data) or 'query_dwr_api' (which might be a more general query tool). The description lacks any context on prerequisites, exclusions, or recommended scenarios.
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 retrieving data but doesn't specify whether this is a read-only operation, if it requires authentication, rate limits, error handling, or the format of the returned data (e.g., JSON, CSV). This leaves significant gaps for an AI agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
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 directly communicates the tool's function, making it easy to parse and understand quickly.
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 data retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the returned time series data includes (e.g., water level, flow rate), how it's structured, or any limitations (e.g., date range constraints, data availability). This leaves the AI agent with incomplete information for proper usage.
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 schema description coverage is 100%, so the input schema already documents all three parameters (abbrev, startDate, endDate) with their types and formats. The description adds no additional semantic context beyond implying that 'abbrev' refers to a surface water station, which is already clear from the tool name and schema. This meets the baseline for high schema coverage.
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') and resource ('daily time series data for a surface water station'), making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from its sibling 'get_surface_water_stations', which might retrieve station metadata rather than time series data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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_surface_water_stations' or 'query_dwr_api'. It lacks context about prerequisites, such as needing a valid station abbreviation, or exclusions, such as not supporting real-time data.
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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