source-coop-mcp

by yharby

Overview Schema Related Servers Score Discussions

Python

Remote

Server Quality Checklist

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Latest release: v0.2.6
Disambiguation5/5
Each tool has a clear, distinct purpose: metadata retrieval for files vs. products, listing accounts, products, or files, and a separate search function. No overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (get_file_metadata, list_accounts, search, etc.) using lowercase with underscores. Highly predictable and uniform.
Tool Count5/5
Six tools is ideal for a data catalog server, covering discovery, browsing, and search without being overwhelming or too sparse.
Completeness5/5
The tool surface covers all essential operations for exploring a data cooperative: listing accounts, products, files, retrieving detailed metadata, and cross-account search. No obvious gaps.
Average 4.2/5 across 6 of 6 tools scored. Lowest: 3.6/5.
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
- Last stable release on November 9, 2025
- 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.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that the tool always includes README content if found, and summarizes return fields (account info, storage config, roles, tags). However, it does not mention side effects (likely none, read-only), error behavior for missing products, or permission needs. The description adds moderate behavioral context.
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 and well-structured: a single-line summary, a key note about README, then Args, Returns, and Example sections. Every sentence adds value without redundancy. It is appropriately sized for the tool's simplicity.
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 (2 string params, no enums, output schema exists), the description covers the main functionality, return structure summary, and includes an example. It lacks error handling details or explicit read-only indication, but for a straightforward retrieval tool it is largely complete.
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?
Input schema has 0% description coverage, so the description must compensate. It provides examples for account_id ('harvard-lil') and product_id ('gov-data') in the Args section and a usage example. However, it does not explain the nature of these IDs (e.g., account name vs. identifier) or valid formats. The examples add partial semantics but not full detail.
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 'Get comprehensive metadata for a specific product' with a strong verb and resource. It distinguishes from sibling tools like get_file_metadata (file-level) and list_products (list of products) by focusing on a single product's full details, including README.
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. For example, it does not explain that this tool is for detailed retrieval of a known product versus using list_products for summaries or get_file_metadata for file-level info. The description lacks explicit 'when to use' or 'when not to use' 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?
No annotations are provided, so the description carries the full burden. It does not disclose whether the operation is read-only, requires authentication, or has any side effects. The return format is given but not the 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 extremely concise: one sentence plus a returns line and example. The purpose is front-loaded, and every element is necessary.
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?
For a parameterless list tool, the description is almost complete. It provides the return format and an example. However, it does not mention edge cases like empty lists or pagination, which are minor gaps.
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?
There are no parameters, so the schema coverage is 100% by default. The description adds no parameter documentation but does not need to. Baseline score of 4 is appropriate.
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 discovers all organizations/accounts, with a concrete return format and example. It is easily distinguishable from sibling tools that deal with files and products.
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 or when not to use this tool. There is no mention of alternatives or prerequisites, leaving the agent without context for selection.
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?
No annotations are provided, so the description carries full burden. It mentions using 'obstore's head operation for efficient metadata retrieval' and 'without downloading,' implying read-only behavior. However, it does not disclose authentication needs, rate limits, or potential errors. This is moderately transparent but lacks full disclosure.
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 clear bullet list for args, returns, and an example. No redundant information, 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?
For a simple metadata retrieval tool with one parameter and an output schema, the description covers the purpose, parameter format, and return fields through the example. Missing edge cases like error handling or permissions, but overall complete for the task.
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 schema has 0% description coverage, so the description compensates well: it explains the path parameter accepts S3 URIs or relative paths (account_id/product_id/file) and provides an example. This adds meaningful context beyond the schema's simple 'string' type.
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 'Get metadata for a specific file without downloading it.' It uses a specific verb and resource, and distinguishes from sibling tools (list_product_files, search, etc.) which focus on listings or different entities.
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 implies usage for efficient metadata retrieval without downloading, but does not explicitly state when not to use or compare with alternative tools like get_product_details. Given the tool's specificity, this is adequate but could be improved.
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?
No annotations provided, so description carries full burden. It discloses that the tool returns either files list or tree visualization (not both) to save tokens, and explains token optimization and partition detection. However, it does not mention rate limits, authentication, or 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with Args, Returns, and multiple examples. While lengthy, each part adds value. A slightly more concise introduction could improve, but overall it is front-loaded and informative.
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 complexity (optional modes, token optimization, partition detection) and that output schema exists, the description is very complete. It covers all parameters, behavior, and edge cases like partitioned data summarization. No gaps identified.
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 coverage is 0% (no parameter descriptions in schema). The description adds comprehensive explanations for each parameter: account_id, product_id, prefix, max_files, show_tree, including defaults and purpose. Examples further clarify usage.
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 'List all files in a product with full S3 paths ready for analysis', specifying the verb 'list' and resource 'product files'. It differentiates from siblings like 'get_file_metadata' (single file) and 'search' (search across products).
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?
Examples show when to use tree mode vs list mode (token efficiency), and mention that results are either files or tree. However, it does not explicitly state when not to use this tool or compare to alternatives like 'search'.
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?
With no annotations, description fully carries behavioral disclosure burden. It details two operation modes, performance (~240ms vs ~500ms), parameter effects (include_unpublished, featured_only, include_file_count), and return formats. No contradictions.
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?
Description is lengthy but well-structured with sections (DEFAULT, Args, Returns, Performance, Examples). Information is front-loaded. Could be slightly more concise, but organization aids readability.
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 complexity (4 parameters, two modes, rich behavior) and absence of annotations, description is thorough. It covers parameter semantics, return formats, performance, and provides multiple examples. Output schema exists but description adds value beyond it.
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 has 0% description coverage, so description must compensate. It explains each parameter's purpose, defaults, and interactions (e.g., 'account_id required for S3 mode', 'include_unpublished switches modes'). Fully compensates for missing schema descriptions.
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 it lists products (datasets) in Source Cooperative, distinguishing between S3 direct scan and API modes. The verb 'list' and resource 'products' are specific, and the description differentiates from sibling tools like get_product_details and list_accounts.
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?
Provides explicit guidance on when to use each mode (S3 mode default, fast, includes all products; API mode for published-only with rich metadata). Examples and parameter explanations help. Lacks explicit mention of when not to use this tool vs alternatives, but overall clear.
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?
With no annotations provided, the description fully discloses behavioral traits: fuzzy matching with 60% similarity threshold, hybrid search across 94+ organizations and all products, metadata differences for published vs unpublished, performance details (5-8s, 11x faster), and result structure with examples.
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 sections (details, args, returns, performance, examples) and front-loads the core purpose. While somewhat lengthy, every part adds value (especially the examples). Could be slightly more concise but remains effective.
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 complexity (fuzzy search across accounts/products, handling unpublished items, performance characteristics), the description covers all aspects thoroughly. It includes output schema details in examples, addresses edge cases (unpublished products), and provides usage guidance, making it complete for effective 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?
The schema has 1 parameter with 0% description coverage, but the description explains 'query' as 'Search keyword (supports typos and partial matches)' and provides multiple examples of its usage. This adds significant meaning beyond the schema definition.
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 'Search for products across ALL accounts with smart fuzzy matching', specifying the verb, resource, and key differentiating features like fuzzy matching and cross-account search. It distinguishes from sibling tools that likely do exact listing or detail retrieval.
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 extensive guidelines on when to use this tool: for fuzzy searching, handling typos, and partial matches across all accounts and products (published and unpublished). It explains what is searched and the return format but does not explicitly state when not to use it or provide direct comparisons to sibling tools.
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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