AviBase MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes focused on different query dimensions (e.g., region, taxonomy, conservation status), but some overlap exists between 'custom_bird_query' and 'execute_jsonata_query' as both handle advanced queries, which could cause confusion. The descriptions help differentiate them, with 'custom_bird_query' being filter-based and 'execute_jsonata_query' for raw JSONata, but agents might still misselect.
Naming Consistency4/5
Tool names follow a consistent verb_noun pattern (e.g., get_birds_by_region, search_birds) with clear, descriptive terms. The main deviation is 'execute_jsonata_query', which uses 'execute' instead of 'get' or 'search', but this is minor and still readable within the overall pattern.
Tool Count5/5
With 11 tools, the count is well-scoped for a bird data server, covering various query types and analyses without being overwhelming. Each tool earns its place by addressing specific aspects like geography, taxonomy, or statistics, making it appropriate for the domain.
Completeness4/5
The tool set provides comprehensive coverage for querying and analyzing bird data, including filters by region, taxonomy, conservation status, and advanced queries. A minor gap is the lack of tools for updating or managing data (e.g., create/delete records), but as a read-only query server, it effectively covers the intended domain with no dead ends.
Average 3/5 across 11 of 11 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 is licensed under ISC License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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 mentions 'complex queries' and 'advanced analysis,' but lacks details on permissions, rate limits, response format, or error handling. This is inadequate for a tool with nested parameters and no output schema, leaving key operational traits unspecified.
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 conveys the core function without unnecessary words. It is front-loaded with the main action ('Perform complex queries'), making it easy to parse, though it could be slightly more structured for clarity.
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 (nested objects, no output schema) and lack of annotations, the description is incomplete. It doesn't explain what 'advanced analysis' entails, how results are returned, or any behavioral constraints, leaving significant gaps for effective tool use in context with siblings.
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 fully documents the two parameters ('filters' and 'limit'). The description adds no additional meaning beyond implying 'multiple filters,' which is already clear from the schema. This meets the baseline for high coverage without extra value.
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 performs 'complex queries with multiple filters for advanced bird data analysis,' which indicates a querying function but is somewhat vague. It specifies the resource ('bird data') and mentions filtering, but doesn't clearly differentiate from sibling tools like 'search_birds' or 'get_birds_by_taxonomy,' leaving ambiguity about its unique role.
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. With siblings like 'search_birds' and 'get_birds_by_taxonomy,' it fails to specify scenarios, prerequisites, or exclusions, offering only a general statement about 'advanced analysis' without practical 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 for behavioral disclosure. It states the tool executes queries but doesn't mention what data source it queries, what permissions are required, whether there are rate limits, what happens with malformed queries, or what the output format looks like. The description is too vague about the actual behavior.
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 two sentences that each add value. The first sentence states the core purpose, and the second provides context about JSONata's capabilities. There's no wasted text, though it could be more front-loaded with critical usage information.
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, no output schema, and 2 parameters (one optional), the description is insufficient. It doesn't explain what data is being queried (bird data based on siblings?), what the return format is, error handling, or how this differs from the many specialized bird query tools. The context signals suggest this is part of a bird-related API, but the description doesn't acknowledge this domain context.
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 adds no additional parameter information beyond what's in the schema. It mentions JSONata generally but doesn't provide context about the 'query' parameter format or 'limit' usage beyond the schema's default value.
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 as 'Execute a raw JSONata query for advanced data analysis and transformation' with a specific verb ('execute') and resource ('JSONata query'). It distinguishes from sibling tools by mentioning JSONata specifically, but doesn't explicitly contrast with the various bird-related sibling tools that appear to be domain-specific queries.
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 the many sibling tools (like custom_bird_query, get_bird_report, etc.). It mentions JSONata is for 'advanced data analysis and transformation' but gives no context about when raw JSONata is preferred over the specialized bird query 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. While it mentions what the tool returns ('detailed report'), it doesn't describe important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or response format. The description is insufficient for a tool with no annotation coverage.
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 clearly states the tool's purpose. It's appropriately sized for a simple lookup tool and front-loads the essential information without unnecessary elaboration.
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 mentions returning a 'detailed report' but provides no information about what that report contains, its structure, or any behavioral constraints. Given the lack of structured metadata, the description should do more to help an agent understand what to expect from this tool.
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%, with the single parameter 'scientific_name' fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema (e.g., it doesn't clarify format requirements or provide examples). With high schema coverage, 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.
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 a specific verb ('Get') and resource ('detailed report for a specific bird species'), and it specifies the content includes 'related species and comprehensive information'. However, it doesn't explicitly differentiate from sibling tools like 'get_bird_stats' or 'get_conservation_status' that might also provide bird information.
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. With multiple sibling tools like 'get_birds_by_region', 'get_bird_stats', and 'search_birds', there's no indication of when this detailed species report is preferred over other bird-related queries.
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. The description mentions what the tool does but doesn't disclose any behavioral traits such as whether it's read-only, potential rate limits, authentication requirements, error conditions, or what the output looks like (e.g., list format, pagination). For a query tool with no annotation coverage, this leaves significant gaps in understanding how it 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 that front-loads the core purpose ('Find birds described by a specific taxonomic authority') and includes helpful examples in parentheses. There's no wasted verbiage, repetition, or 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 the tool's complexity (a query with filtering), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like safety, performance, or output format, which are crucial for an AI agent to use it correctly. While the purpose is clear, the absence of annotations and output details means the description should do more to compensate, which it doesn't.
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 both parameters (authority, limit) well-documented in the schema. The description adds minimal value beyond the schema by providing examples of authorities (Linnaeus, Darwin), which slightly clarifies the expected format. However, it doesn't explain parameter interactions, constraints, or usage nuances, so it meets the baseline for high schema coverage without significant enhancement.
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 'Find' and resource 'birds' with specific filtering criteria 'described by a specific taxonomic authority', making the purpose immediately understandable. It provides examples (Linnaeus, Darwin) to clarify the authority parameter. However, it doesn't explicitly distinguish this tool from sibling tools like 'get_birds_by_taxonomy' or 'search_birds', which might also involve taxonomic filtering.
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. With multiple sibling tools involving bird queries (custom_bird_query, get_birds_by_region, get_birds_by_taxonomy, search_birds), there's no indication of when this authority-based filtering is preferred over other filtering methods or what makes it unique in the toolset.
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 states what the tool does but doesn't describe how it behaves: no information about return format (e.g., list of birds with what fields?), pagination (implied by 'limit' parameter but not explained), error handling, or performance characteristics. The description is functional but lacks operational context needed for an agent to use it effectively.
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—a single sentence with helpful examples in parentheses. Every word earns its place: 'Find birds by geographic region or range' establishes the core function, and '(e.g., Madagascar, Australia, Africa, etc.)' provides concrete guidance without verbosity. It's front-loaded with the essential information.
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 and no output schema, the description is incomplete for a tool with two parameters and multiple siblings. It doesn't explain what the tool returns (bird objects? names only?), how results are structured, or how it differs from similar tools like 'search_birds'. For a query tool in a rich sibling environment, more contextual information would help an agent use it correctly.
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 ('region' and 'limit'). The description adds no additional parameter semantics beyond what's in the schema—it doesn't clarify region format (e.g., are continent names like 'Africa' valid?), search behavior (exact match vs. substring), or result ordering. 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 tool's purpose: 'Find birds by geographic region or range' with specific examples. It uses a clear verb ('Find') and resource ('birds') with a specific scope ('by geographic region or range'). However, it doesn't explicitly differentiate from sibling tools like 'search_birds' or 'get_birds_by_taxonomy', 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 sibling tools like 'search_birds' (which might allow more flexible queries) or 'get_birds_by_taxonomy' (which filters by classification). There's no context about prerequisites, exclusions, or comparative advantages.
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 retrieves data ('Get'), implying a read-only operation, but doesn't disclose any behavioral traits such as rate limits, authentication needs, pagination, or what the return format looks like (e.g., list structure, fields included). For a tool with zero 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: 'Get all extinct or possibly extinct bird species.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's simplicity. Every part of the sentence earns its place by specifying the action, scope, and resource.
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 low complexity (one optional parameter) and lack of annotations and output schema, the description is incomplete. It adequately states what the tool does but fails to provide necessary context such as the return format, error handling, or any behavioral constraints. For a tool with no structured data beyond the input schema, the description should do more to compensate for these 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?
The input schema has 100% description coverage, with the 'limit' parameter fully documented in the schema itself. The description adds no additional meaning beyond what the schema provides—it doesn't explain parameter interactions, default behavior beyond the schema's default, or semantic context. With high schema coverage, 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 all extinct or possibly extinct bird species.' It specifies the verb ('Get'), resource ('bird species'), and scope ('extinct or possibly extinct'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_conservation_status' or 'get_bird_report', which might also involve conservation status 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. It doesn't mention sibling tools like 'get_conservation_status' (which might provide broader conservation data) or 'search_birds' (which could filter for extinct species), nor does it specify prerequisites or exclusions. Usage is implied by the purpose but not explicitly defined.
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 returns a 'random sample' but does not explain how randomness is implemented, whether results are reproducible, what data fields are included in the output, or any rate limits or authentication requirements. This leaves significant gaps in understanding 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, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core action ('Get a random sample of birds') and adds a brief rationale ('for exploration and discovery'), making it easy to 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 lack of annotations and output schema, the description is incomplete. It does not address key behavioral aspects like output format, randomness mechanism, or error handling. For a tool with no structured metadata, the description should provide more context to compensate, but it falls short, leaving the agent with insufficient information for reliable 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 'count' parameter well-documented in the schema (including default and maximum values). The description does not add any parameter-specific information beyond what the schema provides, such as clarifying the meaning of 'random' or output details. With high schema coverage, 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.
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 a specific verb ('Get') and resource ('random sample of birds'), and mentions the goal ('for exploration and discovery'). However, it does not explicitly distinguish this tool from its siblings like 'get_birds_by_region' or 'search_birds', which might also involve retrieving birds but with different filtering criteria.
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 'exploration and discovery' but does not specify scenarios where random sampling is preferred over structured queries like 'get_birds_by_region' or 'search_birds', nor does it mention any prerequisites or exclusions for usage.
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 'fuzzy matching support' and implies a search operation, but lacks details on permissions, rate limits, error handling, or what the response looks like (e.g., result format, pagination). For a search tool with zero annotation coverage, this leaves significant behavioral gaps.
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 functionality ('Search for birds') and includes essential details without waste. Every word contributes to understanding the tool's purpose and key feature.
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 moderate complexity (search with parameters), no annotations, and no output schema, the description is incomplete. It lacks information on behavioral traits (e.g., safety, performance), output format, and differentiation from siblings, making it inadequate for full agent understanding in this context.
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 fully documents all three parameters (query, exact, limit). The description adds minimal value beyond the schema by hinting at 'fuzzy matching' (related to the 'exact' parameter) but does not provide additional syntax or format details. 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 tool's purpose with a specific verb ('Search for birds') and resource ('birds'), and specifies search criteria ('by scientific or common name') and a key feature ('fuzzy matching support'). However, it does not explicitly differentiate this tool from its many siblings (e.g., get_birds_by_region, get_birds_by_taxonomy), which limits its clarity in a crowded toolset.
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 its siblings, such as custom_bird_query or get_birds_by_region. It implies usage for name-based searches but does not specify exclusions or alternatives, leaving the agent to infer context without explicit 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions filtering but doesn't describe the return format (e.g., list of birds with details), pagination behavior, error handling, or any rate limits. For a query tool with zero annotation coverage, this leaves 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 that front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, resource, and filtering criteria, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a query tool with 3 parameters. It covers the basic purpose but lacks details on return values, error conditions, or behavioral traits. However, the high schema coverage partially compensates, making it minimally adequate but with clear gaps in context.
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 parameters (level, value, limit) with descriptions and examples. The description adds no additional parameter semantics beyond what's in the schema, such as explaining the relationship between level and value or edge cases. 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' and resource 'birds' with specific filtering criteria 'by taxonomic classification', making the purpose evident. It distinguishes from siblings like 'get_birds_by_region' or 'get_birds_by_authority' by specifying the taxonomic focus, though it doesn't explicitly contrast with all 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?
The description provides no guidance on when to use this tool versus siblings like 'search_birds' or 'custom_bird_query', nor does it mention prerequisites or exclusions. It implies usage for taxonomic filtering but lacks explicit context or alternatives, leaving the agent to infer based on tool names alone.
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 mentions the tool 'gets' birds (implying a read operation) but doesn't disclose behavioral traits like whether it requires authentication, rate limits, pagination behavior, or what the return format looks like (e.g., list of bird names, detailed records). For a tool with no annotations, this leaves significant gaps in understanding how it 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 that front-loads the core purpose and includes helpful parenthetical explanations of category codes. Every word earns its place with no redundancy or unnecessary details, making it easy to parse quickly.
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?
Given no annotations and no output schema, the description is incomplete for a tool that returns data. It covers the purpose and parameters well but lacks information on what the output contains (e.g., bird names, full records, error handling) and behavioral aspects like rate limits. For a read operation with two parameters, this leaves the agent without full context on how to interpret results.
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 (category with enum values and limit with default). The description adds minimal value beyond the schema by listing some category examples (CR, EN, VU, EX, etc.), but doesn't explain parameter interactions or provide additional context like what 'etc.' includes. 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.
Purpose5/5
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 ('birds'), specifies the filtering criterion ('by IUCN Red List conservation status'), and distinguishes from siblings by focusing on conservation status rather than taxonomy, region, or other attributes. It provides specific category codes (CR, EN, VU, etc.) that help differentiate it from tools like get_extinct_species or get_birds_by_region.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (when querying birds by conservation status) but doesn't explicitly state when not to use it or name alternatives. For example, it doesn't clarify that get_extinct_species might be a more specific alternative for extinct birds, or when to use search_birds for broader queries. The context is clear but lacks explicit exclusions or comparisons.
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?
With no annotations provided, the description carries full burden. It clearly indicates this is a read operation ('Get') and specifies what data is returned, but doesn't disclose behavioral aspects like performance characteristics, rate limits, authentication requirements, or whether the statistics are real-time or cached. The description adds value by specifying the scope of statistics but lacks operational 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 a single, well-structured sentence that efficiently communicates the tool's purpose and output scope. Every word earns its place - 'comprehensive statistics' sets expectations, and the list of specific metrics provides complete information without redundancy or unnecessary elaboration.
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 zero-parameter tool with no output schema, the description adequately covers what statistics are returned. However, without annotations or output schema, it doesn't provide information about return format, data freshness, or error conditions. The description is complete for basic understanding but lacks operational details that would be helpful for an AI agent.
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?
With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, and instead focuses on what the tool returns. It adds semantic value by detailing the specific statistical categories included in the output.
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 specific action ('Get comprehensive statistics') and resource ('bird dataset'), listing exactly what statistics are included (total records, species count, families, orders, conservation categories). It distinguishes this from siblings like get_birds_by_region or search_birds by focusing on aggregated dataset metrics rather than filtered queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining high-level dataset metrics, but doesn't explicitly state when to use this versus alternatives like custom_bird_query or get_bird_report. It provides context about what statistics are available but lacks explicit guidance on when this tool is preferred over other statistical or query 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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