BigContext MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation2/5
Multiple tools have overlapping purposes that could cause confusion. For example, 'audit_cognitive_operations', 'detect_inference_violations', and 'get_epistemological_report' all involve detecting or preventing unauthorized cognitive operations, making it hard for an agent to choose the right one. Similarly, 'validate_claim', 'validate_literal_quote', and 'validate_output_vocabulary' all validate different aspects of textual claims, but their boundaries are not clearly distinct, leading to potential misselection.
Naming Consistency4/5The naming is mostly consistent with a verb_noun pattern (e.g., 'analyze_subdetermination', 'detect_pattern_contamination', 'validate_agency_execution'), which aids readability. However, there are minor deviations like 'get_metadata' and 'list_documents' using simpler verbs, and 'ingest_document' using 'ingest' instead of a more common verb like 'load', but these do not significantly hinder understanding.
Tool Count2/5With 31 tools, the count is too high for a coherent set, making the server feel heavy and overwhelming. The tools cover a wide range of functions from document ingestion to complex epistemological analysis, but many could be consolidated or omitted without losing core functionality, indicating poor scoping and an excessive number that complicates agent usage.
Completeness5/5The tool set provides comprehensive coverage for textual analysis and validation in a scholarly or theological domain. It includes ingestion, search, metadata retrieval, various detection and validation tools, and safe fallback mechanisms, ensuring no obvious gaps. The tools support a full lifecycle from document loading to rigorous claim validation, making the surface complete for its intended purpose.
Average 3.7/5 across 31 of 31 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
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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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
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. It states the tool retrieves permitted operations based on genre, but lacks critical behavioral details: what permissions or authentication are required, whether it's a read-only operation, what happens if the segment_id is invalid, or if there are rate limits. The description is too vague about the tool's behavior beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences: purpose statement, context about genres, and parameter/return notes. It's front-loaded with the core purpose. However, the 'Args' and 'Returns' sections are somewhat redundant with the schema and could be integrated more smoothly into the narrative flow.
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?
Given 1 parameter with 0% schema coverage and an output schema exists, the description is minimally adequate. The output schema means return values don't need explanation, but the description fails to provide genre examples, operation types, or error handling. For a tool with no annotations and low schema coverage, more context about inputs and behavior would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only mentions 'segment_id: ID of the segment to check' in the Args section, which repeats the parameter name without adding meaningful semantics. It doesn't explain what a segment is, how IDs are structured, or provide examples. This leaves the single parameter poorly documented.
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 tool's purpose: 'Get permitted cognitive operations based on text genre.' It specifies the verb ('Get'), resource ('permitted cognitive operations'), and key factor ('based on text genre'). However, it doesn't explicitly differentiate from sibling tools like 'detect_text_genre' or 'check_language_operation', which appear related to genre and operations analysis.
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 minimal guidance: it mentions that different genres allow different operations, but doesn't specify when to use this tool versus alternatives. For example, it doesn't clarify if this should be used before or after 'detect_text_genre', or how it differs from 'audit_cognitive_operations'. No explicit when/when-not instructions or alternative tool references are provided.
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 tool returns analysis results but doesn't describe what those results look like, potential side effects, permissions needed, or performance characteristics. The description adds minimal behavioral context beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences: purpose statement, return value explanation, and parameter documentation. It's front-loaded with the core functionality and avoids unnecessary elaboration, though the structure could be slightly more polished.
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?
Given the tool has an output schema (which handles return values), one parameter with basic documentation in the description, and no annotations, the description is minimally adequate. However, for a tool performing complex textual analysis, it lacks details about input validation, error conditions, or example usage that 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.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description includes an 'Args' section that documents the single parameter 'segment_id' as the ID of the segment to analyze. This adds meaningful semantics beyond the bare schema, though it doesn't explain format constraints or provide examples. With only one parameter, the baseline is appropriately met.
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 tool analyzes textual ambiguity to distinguish between total indeterminacy and directed subdetermination, specifying it returns what the text closes vs. leaves open and detects asymmetric relations. This is a specific verb+resource combination, though it doesn't explicitly differentiate from sibling tools like 'detect_weak_quantifiers' or 'validate_claim' which might overlap in semantic analysis.
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 prerequisites, context, or exclusions, leaving the agent to infer usage from the purpose alone without explicit comparison to sibling 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?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states what the tool does (detects frames) and a benefit (prevents reductive analysis), but doesn't disclose permissions needed, rate limits, whether it's read-only or mutative, error conditions, or processing characteristics. The description doesn't contradict annotations since none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with 4 sentences. It's front-loaded with the core purpose, followed by details. The 'Args' and 'Returns' sections provide structured parameter and output information, though some sentences could be more efficient (e.g., 'Prevents reductive analysis...' could be integrated better).
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?
Given 2 parameters with 0% schema coverage and an output schema exists, the description partially compensates by documenting parameters and stating the return type. However, for a tool with no annotations and complex sibling tools, it lacks sufficient context about behavioral traits, error handling, and differentiation from alternatives. The output schema reduces but doesn't eliminate completeness needs.
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 0%, so the description must compensate. It lists both parameters (segment_id, query) with brief explanations, adding meaning beyond the bare schema. However, it doesn't explain parameter constraints, formats, or relationships. With 2 parameters documented but not richly, this meets the baseline for partial compensation.
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 tool's purpose: 'Detect conceptual frameworks in a text segment' with specific frame types listed (causal, revelational, performative, invocative). It distinguishes from some siblings like 'detect_performatives' by covering multiple frame types, but doesn't explicitly differentiate from all similar tools like 'detect_narrative_voice' or 'detect_text_genre'.
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 on when to use this tool versus alternatives is provided. The description mentions 'Prevents reductive analysis by identifying non-causal categories' which hints at a use case, but doesn't specify when to choose this over sibling tools like 'detect_performatives' or 'analyze_subdetermination'.
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 is 'domain-agnostic' and that patterns are provided 'dynamically,' but fails to disclose critical behavioral traits such as whether this is a read-only analysis, potential side effects, performance characteristics, or error handling. The description adds minimal context beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first, followed by domain context and parameter details. Each sentence adds value, though the parameter section could be more structured. There's minimal waste, but it's not perfectly optimized for quick scanning.
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?
Given the complexity of pattern detection, no annotations, and an output schema (which reduces the need to explain returns), the description is moderately complete. It covers the purpose and parameters at a high level but lacks details on behavioral traits and deeper parameter semantics, leaving gaps for an AI agent to infer usage correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It lists the three parameters ('claimed_output,' 'segment_id,' 'patterns') and briefly explains 'patterns' as 'Optional: Pattern definitions with trigger/expectedCompletion,' but doesn't clarify the meaning of 'claimed_output' or 'segment_id,' nor provide examples or constraints. This is insufficient for a 3-parameter tool with no schema documentation.
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 tool's purpose: 'Detect when output may be completing a known pattern not in source.' It specifies the verb 'detect' and the resource 'pattern contamination,' but doesn't explicitly differentiate from sibling tools like 'detect_narrative_voice' or 'detect_semantic_frames' beyond mentioning it's 'domain-agnostic.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating it's 'domain-agnostic' and that 'Agent provides patterns dynamically based on document genre,' suggesting it's for pattern detection across genres. However, it lacks explicit guidance on when to use this tool versus alternatives like 'detect_inference_violations' or 'validate_claim,' and doesn't specify exclusions or prerequisites.
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. It mentions the tool 'detects' and 'identifies' patterns, implying a read-only analysis operation, but doesn't disclose any behavioral traits like whether it's computationally intensive, what permissions are needed, error conditions, or how results are structured beyond 'Performative detection result.' For a detection tool with no 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences: purpose statement, pattern explanation, and parameter/return documentation. Each sentence adds value without redundancy. The structure is front-loaded with the core purpose, though the parameter and return sections could be slightly more integrated with the main description.
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?
Given one parameter with 0% schema coverage but clarified in description, and an output schema exists (so return values don't need description), the description is minimally adequate. However, for a detection tool with no annotations and many sibling alternatives, it lacks context about performance characteristics, error handling, or integration with other tools. The description covers basics but leaves operational gaps.
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?
Schema description coverage is 0% (no parameter descriptions in schema), but the description compensates by explaining the single parameter 'segment_id' as 'ID of the segment to analyze.' This adds meaningful context beyond the schema's type information. With only one parameter, the description adequately clarifies its purpose, though it doesn't specify format constraints or valid ranges.
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 tool's purpose: detecting performative speech acts where divine speech is the creative act, specifically identifying 'And God said... and it was so' patterns. It distinguishes from siblings by focusing on performative speech acts rather than other analysis types like detecting divine agency without speech or checking anachronisms. However, it doesn't explicitly contrast with all similar siblings like 'detect_narrative_voice' or 'identify_speaker'.
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. With many sibling tools for text analysis (e.g., detect_divine_agency_without_speech, detect_narrative_voice, detect_semantic_frames), there's no indication of when performative speech detection is appropriate versus other detection methods. The description mentions the tool 'resists causal analysis' but doesn't explain what analytical context makes this tool preferable.
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. It mentions the tool is 'CRITICAL' and describes the core detection logic (separating speech vs. action verbs), but lacks details on behavioral traits like error handling, performance characteristics, rate limits, or what constitutes a 'detection result'. For a tool with no annotation coverage, this leaves significant gaps in understanding its operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (CRITICAL notice, domain-agnostic note, examples, args, returns). It's front-loaded with key information and uses bullet points for readability. While efficient, the 'CRITICAL' label might be slightly dramatic without supporting context, but overall it avoids unnecessary verbosity.
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?
Given the tool's complexity (detection logic with 3 parameters) and no annotations, the description covers purpose, parameters, and return at a high level. However, with an output schema present, it doesn't need to detail return values. The main gap is lack of behavioral context (e.g., how detection works algorithmically, error cases), making it minimally adequate but not fully comprehensive for safe 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 0%, so the description must compensate. It lists and briefly explains all three parameters (segment_id, agent_patterns, domain_vocabulary), adding meaning beyond the bare schema. However, it doesn't provide detailed semantics like format examples for agent_patterns or domain_vocabulary structure, leaving some ambiguity for implementation.
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 tool's purpose: detecting when agents act without speaking by separating speech verbs from action verbs. It provides specific examples (Biblical 'God remembered Noah', Legal 'the Court ruled'), making the function concrete. However, it doesn't explicitly distinguish this tool from sibling tools like 'identify_speaker' or 'detect_performatives', which might have overlapping domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through examples (Biblical, Legal domains) and mentions it's 'DOMAIN-AGNOSTIC' with dynamic agentPatterns, suggesting flexibility across contexts. However, it lacks explicit guidance on when to use this tool versus alternatives like 'identify_speaker' or 'detect_semantic_frames', and doesn't specify prerequisites or exclusions.
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 'Get's metadata, implying a read-only operation, but doesn't clarify permissions, rate limits, or error handling. The mention of 'top terms by TF-IDF' adds some context, but key behavioral traits like whether it's idempotent or safe are missing, leaving gaps for a tool with parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a purpose statement, bullet-pointed arguments, and a returns section. It's appropriately sized at four sentences, with each sentence adding value (e.g., specifying TF-IDF, explaining parameters). Minor improvements could include front-loading key details more, but overall it's efficient with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no annotations, but with an output schema), the description is fairly complete. It covers purpose, parameters, and return values, and the output schema reduces the need to detail response structure. However, it lacks behavioral context (e.g., error cases) and sibling differentiation, which holds it back from a perfect score.
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?
Schema description coverage is 0%, so the description must compensate. It provides a clear 'Args' section explaining all four parameters: 'document_id' and 'segment_id' for targeting, 'include_structure' for optional structure inclusion, and 'top_terms' for term count with a default. This adds significant meaning beyond the bare schema, though it could detail parameter interactions (e.g., using both IDs).
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 tool retrieves 'metadata, structure, and statistics for a document or segment' and specifically mentions 'top terms by TF-IDF.' This provides a specific verb ('Get') and resource ('document or segment') with additional details about what metadata includes. However, it doesn't explicitly differentiate from sibling tools like 'list_documents' or 'get_adjacent_segments' beyond the metadata focus.
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 prerequisites, such as needing an existing document ID from 'list_documents' or 'ingest_document,' or contrast it with siblings like 'analyze_subdetermination' or 'get_epistemological_report' that might handle different aspects of document analysis. Usage is implied by the purpose but not explicitly stated.
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 pagination via 'offset' but does not cover critical aspects like rate limits, authentication needs, error handling, or whether the operation is read-only or has side effects. For a list operation 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured 'Args' and 'Returns' sections. Every sentence earns its place without redundancy, making it efficient and easy to parse.
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?
Given the tool's low complexity (2 parameters, no nested objects) and the presence of an output schema (implied by 'Returns'), the description is somewhat complete but has gaps. It covers basic purpose and parameters but lacks behavioral context and usage guidelines, which are important for a tool with no annotations. The output schema handles return values, but overall completeness is minimal viable.
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 description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'limit' controls the maximum number of documents returned with a default, and 'offset' is for pagination to skip documents. This compensates well for the schema's lack of descriptions, though it could detail format constraints (e.g., integer ranges).
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 tool's purpose: 'List all indexed documents with their metadata.' This specifies the verb ('List'), resource ('indexed documents'), and scope ('all'). However, it does not differentiate from sibling tools like 'get_metadata' or 'search_segment', which might have overlapping functions, so it misses full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 lacks explicit context, exclusions, or references to sibling tools such as 'search_segment' for filtered searches or 'get_metadata' for specific metadata retrieval, leaving usage ambiguous.
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 what the tool does but lacks behavioral details such as performance characteristics (e.g., speed, accuracy), error handling, or any constraints like rate limits. The description is functional but minimal in behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by structured parameter and return explanations. It's efficient with no wasted sentences, though the parameter descriptions could be more detailed without sacrificing conciseness.
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?
Given the complexity (validation tool with 2 parameters), no annotations, and an output schema present (which covers return values), the description is minimally adequate. It explains the purpose and parameters but lacks deeper context like examples, edge cases, or integration with sibling tools, leaving room for improvement.
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 0%, but the description adds basic semantics for both parameters ('document_id: ID of the document', 'output: The output text to validate against document vocabulary'). This compensates partially, though it doesn't detail formats, constraints, or examples, leaving gaps in parameter understanding.
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 tool's purpose with specific verbs ('Check', 'Detects') and resources ('output', 'source document vocabulary'), distinguishing it from siblings like 'validate_claim' or 'validate_literal_quote' by focusing on vocabulary validation rather than other aspects of document analysis.
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 explicit guidance on when to use this tool versus alternatives is provided. While the purpose implies usage for vocabulary validation, there's no mention of prerequisites, context, or comparisons to siblings like 'build_document_vocabulary' or 'detect_semantic_frames' that might overlap in functionality.
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. While it states the tool 'checks' permissions, it doesn't describe what happens during the check, whether it requires authentication, what error conditions might occur, or the format of the permission result. For a permission-checking 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with the core purpose, provides usage guidance, documents parameters with examples, and describes the return value. Every sentence adds value with zero waste, making it easy for an agent to parse.
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?
Given 3 parameters with 0% schema coverage and no annotations, the description provides basic context but lacks depth. The presence of an output schema means the description doesn't need to detail return values, but it should better explain parameter meanings and behavioral aspects. For a permission-checking tool in this context, the description is minimally adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter documentation. The description adds some value by listing the three parameters and providing examples for 'operation' and 'language', but doesn't explain the meaning of 'document_id', valid operation types, or language codes. It partially compensates but leaves key parameter semantics unclear.
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 tool's purpose: 'Check if a specific linguistic operation is allowed.' It specifies the verb ('check') and resource ('linguistic operation'), and distinguishes it from siblings by focusing on permission validation rather than analysis or detection. However, it doesn't explicitly differentiate from 'get_permitted_operations' which might be a related sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'Use before performing morphological, etymological, or text-critical analysis.' This gives practical guidance about the tool's role in a workflow. However, it doesn't specify when NOT to use it or mention alternatives like 'get_permitted_operations' among the siblings.
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 tool's approach ('structural patterns by default') and the optional enhancement with domain vocabulary, but doesn't describe what the detection result looks like, potential limitations, error conditions, or performance characteristics. For a detection 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections: purpose statement, genre list, behavioral note, parameters, and return value. It's appropriately sized at 8 sentences. The information is front-loaded with the core purpose first. Minor redundancy exists with 'Returns:' section when there's an output schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (genre detection with optional enhancement), no annotations, 2 parameters with 0% schema coverage, but with an output schema present, the description does a reasonably complete job. It covers purpose, approach, parameters, and return value. The output schema means the description doesn't need to detail the return structure, which helps completeness.
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 explicitly documents both parameters in the 'Args' section, explaining that segment_id is required and domain_vocabulary is optional for enhanced detection. With 0% schema description coverage, this documentation is essential. However, it doesn't provide format details for domain_vocabulary or explain what a segment_id represents in context, leaving some semantic gaps.
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 tool's purpose: 'Detect text genre to apply correct extraction rules.' It specifies the verb (detect), resource (text genre), and lists the specific genres. However, it doesn't explicitly differentiate this tool from its many siblings, which appear to be various text analysis tools in the same domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some implied usage guidance by mentioning 'DOMAIN-AGNOSTIC: Uses structural patterns by default' and suggesting to 'Provide domainVocabulary for domain-specific enhanced detection.' However, it doesn't explicitly state when to use this tool versus alternatives among the many sibling tools, nor does it provide clear exclusions or prerequisites.
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. It mentions the tool's purpose and returns 'Adjacent segment IDs,' but lacks details on permissions, rate limits, error handling, or whether it's read-only/destructive. For a tool with no annotations, this is insufficient behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by usage guidance and parameter details. It avoids redundancy, but could be slightly more concise by integrating the 'Args' and 'Returns' sections more seamlessly into the flow.
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?
Given no annotations, 0% schema coverage, but an output schema exists, the description is moderately complete. It covers purpose, usage, and parameters, but lacks behavioral context (e.g., safety, limits) and doesn't leverage the output schema to explain return values in more detail, leaving gaps for a tool with adjacency logic.
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?
Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics: 'base_segment_id: The anchor segment ID' and 'max_distance: Maximum distance from base (default: 1).' This clarifies parameter roles beyond the bare schema, though it doesn't fully explain constraints like valid ranges or formats.
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 tool's purpose: 'Get list of segment IDs within proximity constraint.' It specifies the verb ('Get'), resource ('segment IDs'), and constraint ('within proximity'). However, it doesn't explicitly differentiate from sibling tools like 'search_segment' or 'validate_proximity,' 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 Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage guidance: 'Use for extraction queries that require adjacency.' This implies when to use it (for adjacency needs) but doesn't specify when not to use it or name alternatives among siblings (e.g., 'search_segment' or 'validate_proximity'), leaving room for ambiguity.
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 states the tool 'creates' something, implying a write/mutation operation, but doesn't disclose behavioral traits like whether it's idempotent, what permissions are needed, if it modifies the document, or any rate limits. The description adds minimal behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by prerequisite information and parameter/return details. Every sentence adds value, with no redundant information. It could be slightly more structured but is efficient overall.
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?
Given one parameter with 0% schema coverage and an output schema present, the description is minimally complete. It explains the parameter and mentions a return value ('Vocabulary build result'), but lacks details on what the vocabulary entails (e.g., token types, format) or error conditions. For a tool with no annotations, it should provide more behavioral context.
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 adds meaning for the single parameter: 'document_id: ID of the document to build vocabulary from.' This clarifies the parameter's purpose. With 0% schema description coverage, the description compensates adequately for the one parameter, but doesn't provide additional details like format constraints or examples.
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 tool's purpose: 'Build closed vocabulary from document' and 'Creates lexicon of all tokens.' It specifies the verb ('build'), resource ('vocabulary'), and source ('document'). However, it doesn't explicitly differentiate from sibling tools like 'validate_output_vocabulary' beyond mentioning it as a prerequisite.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: 'Required before using validate_output_vocabulary.' This gives explicit guidance on when to use this tool (as a prerequisite). However, it doesn't mention when NOT to use it or alternatives among other siblings like 'list_documents' or 'ingest_document' for related operations.
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. It mentions what the tool does (comparison) and returns (result with similarity and themes), but lacks critical behavioral details: permission requirements, rate limits, whether it's read-only or mutative, error handling, or performance characteristics. For a tool with 4 parameters and no annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized: purpose statement, usage hint, parameter explanations, and return value. Every sentence adds value, with no redundancy. It could be slightly more front-loaded by moving the 'Returns' section closer to the top, but overall it's efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters with 0% schema coverage, no annotations, but an output schema exists, the description does a good job covering basics. It explains all parameters and the return value, and the output schema handles return details. However, for a comparison tool with potential complexity (e.g., bridge-finding), more behavioral context (like performance or limitations) would enhance completeness.
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?
Schema description coverage is 0%, so the description must compensate. It explicitly lists all 4 parameters with brief explanations (e.g., 'ID of the first segment to compare', 'Find intermediate segments that connect the two'), adding meaningful semantics beyond the bare schema. However, it doesn't detail parameter constraints or formats (e.g., what constitutes a valid segment ID), preventing a perfect score.
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 tool's purpose: 'Compare two segments to find shared themes, unique terms, and similarity.' It specifies the verb (compare) and resource (segments) with concrete outputs (themes, terms, similarity). However, it doesn't explicitly differentiate from sibling tools like 'search_segment' or 'get_adjacent_segments', 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 Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some implied usage context: 'Useful for understanding relationships between chapters.' This suggests when to use the tool but doesn't explicitly state when not to use it or name alternatives among siblings. No prerequisites or exclusions are mentioned, leaving gaps in guidance.
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 discloses the return format ('speaker name, confidence level, and evidence') and scope ('domain-agnostic'), which adds useful context. However, it lacks details on behavioral traits like error handling, performance characteristics (e.g., speed, accuracy), or side effects (e.g., whether it modifies data). The description is adequate but not rich in behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with the core purpose, then covers returns, domain scope, parameters, and output in logical order. Every sentence adds value, with no redundant information. It could be slightly more front-loaded by moving the 'Returns' statement earlier, but overall it's efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, 1 required), no annotations, and an output schema present, the description is reasonably complete. It explains the purpose, parameters, returns, and scope. The output schema means the description doesn't need to detail return values, and it covers the essential context. However, it lacks error cases or examples, leaving minor gaps.
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?
Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for all parameters: segment_id ('ID of the segment to analyze'), priority_patterns ('Speaker names to prioritize'), exclude_patterns ('Speaker patterns to flag as ambiguous'), and expected_speaker ('verify this specific speaker'). This clarifies each parameter's role beyond the bare schema, though it doesn't provide format examples or constraints (e.g., pattern syntax).
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 tool's purpose: 'Identify who is speaking in a text segment.' It specifies the verb ('identify') and resource ('speaker in a text segment'), making the function unambiguous. However, it doesn't explicitly differentiate this tool from its many siblings (e.g., detect_narrative_voice, detect_performatives), which could have overlapping functionality, preventing 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. It mentions being 'domain-agnostic' but doesn't specify contexts or prerequisites, nor does it compare to sibling tools like detect_narrative_voice. Without explicit when/when-not instructions or named alternatives, the agent lacks usage direction beyond the basic purpose.
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 the full burden. It discloses that the tool returns snippets with highlighted matches and includes default values for optional parameters, which adds useful behavioral context. However, it doesn't cover important aspects like performance characteristics, error handling, or authentication needs, leaving gaps for a search operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections for purpose, arguments, and returns. It's appropriately sized with no redundant information. Every sentence adds value, though the initial purpose statement could be slightly more front-loaded with key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (5 parameters, no annotations, but with output schema), the description is reasonably complete. It covers the purpose, all parameters with semantics, and mentions the return format. The presence of an output schema means it doesn't need to detail return values extensively, but it could better address usage context and behavioral traits.
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 schema description coverage is 0%, so the description must compensate. It effectively explains all 5 parameters: 'query' as search keywords/phrases, 'document_id' and 'segment_id' as optional scoping filters, 'limit' as maximum results, and 'context_words' as snippet size. This adds substantial meaning beyond the bare schema, though it could provide more detail on parameter interactions.
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 tool searches for relevant segments using TF-IDF and returns snippets with highlighted matches. This specifies both the action (search) and resource (segments) with the TF-IDF method. However, it doesn't explicitly differentiate from sibling tools like 'get_adjacent_segments' or 'compare_segments', which is why it doesn't reach a 5.
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 sibling tools like 'list_documents' for broader searches or 'get_adjacent_segments' for context-based retrieval. There's no indication of prerequisites or typical use cases, leaving the agent without contextual 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'Returns whether the claim requires capabilities the document lacks,' which hints at a read-only, analytical function, but doesn't clarify permissions, rate limits, or potential side effects. For a validation tool with zero annotation coverage, this is insufficient, as it omits key operational details like error handling or performance characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with the core purpose, followed by usage advice, parameter explanations, and return information. Each sentence adds value without redundancy. It could be slightly more concise by integrating the 'Args' and 'Returns' sections more seamlessly, but overall, it's efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (validation tool with 2 parameters, no annotations, but an output schema exists), the description is reasonably complete. It covers purpose, usage, parameters, and return intent. The presence of an output schema means it doesn't need to detail return values, but it could benefit from more behavioral context (e.g., error cases). Overall, it meets most needs for a tool of this type.
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 description adds meaningful context beyond the input schema: it explains that 'document_id' refers to 'the document to validate against' and 'claim' is 'The claim or assertion to validate.' With 0% schema description coverage, this compensates well by clarifying the purpose of each parameter. However, it doesn't detail format constraints (e.g., claim length limits), keeping it from a perfect score.
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 tool's purpose: 'Check if a specific claim can be grounded in the source document.' It specifies the verb ('check'), resource ('claim'), and context ('source document'), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'validate_agency_execution' or 'validate_existential_response', which appear related to validation but target different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage guidance with 'Use this BEFORE making scholarly assertions,' implying a pre-emptive validation step. However, it lacks explicit when-not-to-use criteria or clear alternatives among siblings (e.g., vs. 'validate_agency_execution'). The context is implied but not fully articulated, leaving gaps in distinguishing from similar 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 what the tool detects but lacks details on how it operates (e.g., detection methodology, accuracy, limitations), response format beyond 'Anachronism check result', or any side effects. This is a significant gap 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by specific detection targets and structured parameter/return sections. Every sentence adds value without redundancy, making it efficient and well-organized for quick understanding.
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?
Given the tool's complexity (detection of specific conceptual categories), lack of annotations, and presence of an output schema, the description is moderately complete. It covers purpose and parameter semantics but lacks behavioral details (e.g., how detection works, limitations). The output schema mitigates some gaps, but more context on operation would improve completeness.
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 description adds meaningful context for the single parameter 'query' by specifying it as 'The research question or claim to check', which clarifies its purpose beyond the schema's basic type definition. With 0% schema description coverage and only one parameter, this compensation is effective, though not exhaustive (e.g., no format examples).
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 tool's purpose with a specific verb ('Check') and resource ('research question'), specifying what it detects (Aristotelian causes, Neoplatonic emanation, Trinitarian doctrine). It distinguishes itself from siblings by focusing on anachronisms rather than other analytical tasks like validation or detection of other patterns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking research questions or claims for specific conceptual categories, but does not explicitly state when to use this tool versus alternatives (e.g., other detection or validation tools). No exclusions or prerequisites are mentioned, leaving usage context somewhat vague.
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 explains what the tool validates (executed vs. referenced actions) but does not disclose behavioral traits such as required permissions, rate limits, error handling, or what the validation result entails. For a tool with no annotation coverage, this leaves significant gaps in understanding its operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose and key distinction, followed by examples and parameter explanations. Every sentence adds value, though the biblical references could be slightly verbose; overall, it is efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (validating divine actions), no annotations, 0% schema coverage, but with an output schema present, the description is reasonably complete. It explains the purpose, distinction, and parameters, but lacks details on behavioral aspects like permissions or error handling. The output schema mitigates the need to explain return values, but more context on tool behavior would improve completeness.
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?
Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining 'segment_id' as the ID of the segment to analyze and 'divine_agent_patterns' as optional patterns to identify divine agents, which clarifies their roles beyond the schema's basic types. However, it does not detail format or examples for these parameters.
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 explicitly states the tool's purpose: to validate whether a divine action is executed in-scene versus merely referenced. It provides specific examples (Judges 6:21 for executed, Nehemiah 9:12 for referenced) and clearly distinguishes this from sibling tools like 'detect_divine_agency_without_speech' or 'validate_claim' by focusing on the execution vs. reference distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by explaining the key distinction between executed and referenced actions, but does not explicitly state when to use this tool versus alternatives like 'detect_divine_agency_without_speech' or 'validate_claim'. It provides context but lacks explicit guidance on exclusions or prerequisites.
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 the full burden. It discloses that the tool 'detects parenthetical comments, notes sections, evaluative language,' which adds useful behavioral context beyond basic validation. However, it doesn't cover aspects like error handling, performance implications, or rate limits, leaving some gaps for a mutation-like validation 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 well-structured and front-loaded, starting with the core purpose. Each sentence adds value: the first states the purpose, the second details detection capabilities, the third gives usage guidelines, and the parameter and return sections are clearly separated. There's no wasted text, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (validation tool with 3 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers purpose, usage, and parameters, and the output schema handles return values, so no need to explain those. However, it could benefit from more detail on behavioral aspects like validation rules or error cases, slightly reducing completeness.
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 0%, so the description must compensate. It lists all three parameters ('output', 'fields', 'allow_commentary') and provides a brief explanation for 'allow_commentary' ('Whether commentary is allowed'). However, it doesn't elaborate on the semantics of 'output' or 'fields' (e.g., format, constraints), resulting in a baseline score of 3 as it adds some but incomplete value.
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 tool's purpose: 'Validate that extraction output follows a strict schema.' It specifies the verb 'validate' and the resource 'extraction output,' making it distinct from sibling tools like 'validate_agency_execution' or 'validate_claim.' However, it doesn't explicitly differentiate from all validation siblings, keeping it at 4 rather than 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'Use when user requests pure data extraction.' This gives a specific scenario for when to apply the tool. However, it doesn't mention when not to use it or name alternatives among the many sibling tools, preventing a score of 5.
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 discloses the tool's output components ('language hard stops, canonical frame detection, auto-critique, confidence decay calculation, and recommendations'), which adds valuable behavioral context. However, it doesn't cover critical aspects like error handling, performance characteristics, or side effects, leaving gaps for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by output details and usage guidance. Every sentence adds value, though the 'Args' and 'Returns' sections are somewhat redundant with the schema and could be integrated more smoothly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (epistemological analysis), no annotations, and an output schema exists (so return values needn't be explained), the description does reasonably well. It covers purpose, usage timing, and output components. However, for a tool with 0% schema coverage and no annotations, more parameter details and behavioral context would improve completeness.
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 0%, so the description must compensate. It lists both parameters ('document_id: ID of the document to analyze' and 'query: The research question or claim being investigated'), adding basic semantic meaning beyond the bare schema. However, it doesn't provide format details, constraints, or examples, leaving significant gaps in parameter understanding.
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 tool's purpose: 'Generate complete epistemological analysis before making scholarly claims.' It specifies the verb ('generate') and resource ('epistemological analysis'), and distinguishes it from siblings by focusing on comprehensive pre-analysis. However, it doesn't explicitly differentiate from all 30+ sibling tools, which slightly limits precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use BEFORE any complex textual analysis.' This clearly indicates when to use the tool (as a preparatory step) and implicitly suggests alternatives (other tools for analysis itself). The timing directive is specific and actionable.
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 the full burden. It discloses behavioral traits such as scanning for specific terms and returning a detection result, but does not detail aspects like error handling, performance constraints, or what constitutes a 'violation' beyond listing terms. It adds some context but lacks comprehensive behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with clear sections: purpose (first sentence), details (second sentence), and parameter/return info. Every sentence adds value without redundancy, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, no annotations, output schema exists), the description is mostly complete. It explains the purpose, parameters, and returns, and the output schema handles return values. However, it could benefit from more behavioral context or usage examples to fully compensate for the lack of annotations.
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 input schema has 0% description coverage, but the description compensates by explaining the 'text' parameter as 'The text to scan for inference violations.' This adds meaning beyond the schema's type definition, clarifying the parameter's role. However, it does not elaborate on format or constraints, keeping it at a 4.
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 tool's purpose with specific verbs ('scan', 'detects') and resources ('text', 'inferential connectors', 'prohibited abstract nouns'), and distinguishes it from siblings by focusing on inference violations rather than other linguistic or cognitive operations like anachronisms, narrative voice, or semantic frames.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for scanning text to detect specific inference violations, but does not explicitly state when to use this tool versus alternatives (e.g., other detection tools like detect_pattern_contamination or validate_claim). It provides some context but lacks explicit guidance on exclusions or 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.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behaviors: supported formats, automatic chapter/section detection, default values for parameters, and the 'force' parameter's re-indexing behavior. It doesn't mention rate limits, authentication needs, or error conditions.
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 efficiently structured with a purpose statement, format support, behavioral note, parameter explanations, and return value description - all in compact sentences that earn their place. It's front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (document processing pipeline), no annotations, and an output schema, the description is quite complete. It covers purpose, behavior, parameters, and return value. The main gap is lack of explicit usage guidelines versus sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all 5 parameters in the 'Args' section, providing clear semantics for 'path', 'title', 'chunk_size', 'overlap', and 'force' with defaults and practical meaning beyond the bare schema.
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 tool's purpose with specific verbs ('load, segment, and index') and resource ('a document for search'), distinguishing it from siblings like 'list_documents' or 'search_segment'. It explicitly mentions the document processing pipeline and search orientation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through format support and automatic chapter detection, but doesn't explicitly state when to use this tool versus alternatives like 'list_documents' or 'search_segment'. No explicit exclusions or prerequisites are provided.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a validation tool that detects specific unauthorized operations (synthesis, explanation, causality inference), returns compliance status, and provides safe fallbacks. However, it lacks details on error handling, performance implications, or system dependencies, which are important for a critical pre-response 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 appropriately sized and front-loaded: the first sentence states the critical purpose, followed by key functions and return details. Every sentence earns its place by adding essential information without redundancy, and the structured 'Args' and 'Returns' sections enhance clarity without verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (critical pre-response validation), no annotations, and an output schema present, the description is mostly complete. It covers purpose, usage, and behavioral aspects well, but could improve by detailing parameter semantics more thoroughly to compensate for low schema coverage, ensuring full agent understanding.
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 0%, so the description must compensate. It lists the three parameters (document_id, query, planned_output) and implies their roles in validation, but does not provide detailed semantics (e.g., format of document_id, scope of query). The description adds some meaning by linking parameters to the audit process, but falls short of fully compensating for the schema gap.
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 tool's purpose with specific verbs ('validates cognitive constraint compliance', 'detects unauthorized operations') and resources ('document', 'query', 'planned output'). It distinguishes itself from siblings by focusing on pre-response validation rather than analysis or detection of specific violations like other tools (e.g., detect_inference_violations, validate_agency_execution).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'CRITICAL: Run before ANY response' specifies when to use it (pre-response), and the focus on 'cognitive constraint compliance' and 'unauthorized operations' implies when not to use it (e.g., for post-analysis or non-cognitive tasks). It distinguishes from siblings by its preemptive, compliance-oriented role versus their specific analytical functions.
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 the full burden of behavioral disclosure. It explains what the tool does (detects narrative voice types) and provides workflow guidance, but doesn't disclose performance characteristics, error conditions, rate limits, or authentication requirements. The description adds value by explaining the voice type distinctions and workflow context, but doesn't provide comprehensive behavioral information.
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 efficiently structured with a clear purpose statement, specific voice type distinctions, usage guidance, and parameter explanations. Every sentence adds value, and the information is front-loaded with the most critical details. The 'CRITICAL' label appropriately emphasizes importance without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (narrative voice classification), no annotations, and the presence of an output schema, the description provides good context about what the tool does and when to use it. It explains the four voice types and workflow sequencing, which is essential for proper use. The output schema will handle return value documentation, so the description appropriately focuses on usage context rather than output details.
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?
With 0% schema description coverage for 2 parameters, the description must compensate. It explains that 'segment_id' identifies the segment to analyze and that 'domain_vocabulary' is optional and 'for enhanced detection.' This provides meaningful context about what each parameter represents, though it doesn't specify format requirements or constraints for the domain_vocabulary object.
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 explicitly states the tool's purpose as detecting narrative voice types in text segments, with specific examples of the four voice types it distinguishes. It clearly differentiates this from sibling tools like 'identify_speaker' or 'detect_divine_agency_without_speech' by focusing specifically on narrative voice classification rather than speaker identification or agency detection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'Use BEFORE extracting "divine actions" to avoid confusing retrospective prayer with primary divine agency.' This gives clear context about its sequencing in a workflow and distinguishes it from related tools that might handle different aspects of text analysis.
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 the full burden of behavioral disclosure. It states the tool generates 'safe, compliant' responses for unauthorized operations, implying it's a fallback mechanism rather than performing the actual operation. However, it doesn't disclose details like response format, whether it logs the attempt, rate limits, or authentication requirements. It adds some context but leaves behavioral aspects underspecified.
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 efficiently structured: first sentence states the purpose, second provides usage guidelines, followed by clear 'Args:' and 'Returns:' sections. Every sentence earns its place with no redundant information. It's appropriately sized for a tool with 2 parameters and a straightforward purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no annotations, but with output schema), the description is reasonably complete. It explains purpose, usage, parameters, and return value. The output schema exists, so the description doesn't need to detail return values. However, for a security/compliance tool, additional behavioral context (like logging or audit trails) would enhance completeness.
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 schema description coverage is 0%, so the description must compensate. It provides semantic meaning for both parameters: 'question_type: Type of unauthorized operation (synthesis, explanation, etc.)' and 'document_title: Title of the document for the fallback message.' This clarifies what each parameter represents, though it doesn't specify allowed values or formats. Since there are only 2 parameters and both are documented, this is above baseline.
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 tool's purpose: 'Generate a safe, compliant response when query requires unauthorized operations.' It specifies the verb ('generate'), resource ('safe, compliant response'), and context ('when query requires unauthorized operations'). This distinguishes it from sibling tools like audit_cognitive_operations (which detects violations) and others focused on analysis or validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use when audit_cognitive_operations returns violations.' It provides a clear trigger condition and references a specific sibling tool as the alternative/predecessor. This gives the agent precise guidance on the tool's intended context.
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 the full burden of behavioral disclosure. It explains what constitutes valid vs. invalid responses, but doesn't mention error handling, performance characteristics, or what happens with malformed inputs. It provides basic operational context but lacks depth for a tool with behavioral implications.
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 efficiently structured with clear sections: purpose statement, validation criteria, usage timing, and parameter/return explanations. Every sentence serves a distinct purpose with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (validation logic with specific criteria), no annotations, and an output schema that covers return values, the description provides good context about what the tool does and when to use it. However, it could benefit from examples of valid/invalid responses to fully compensate for the lack of annotations.
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 schema has 0% description coverage for its single parameter, but the description clarifies that 'response' refers to 'The agent response to validate.' This adds meaningful context beyond the bare schema, though it doesn't specify format constraints or examples of valid/invalid responses.
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 tool's purpose: validating responses to existential questions with specific criteria for valid vs. invalid responses. It distinguishes this tool from siblings by focusing on existential question validation rather than other validation types like claim, extraction schema, or literal quote validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use AFTER generating response to existential questions to catch evasion.' This provides clear timing guidance and distinguishes it from other validation tools that serve different purposes.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it detects weak quantifiers, explains why they matter (require statistical evidence), and specifies the return values ('allow', 'require_count', 'block'). However, it lacks details on error handling, performance limits, or edge cases, leaving some behavioral aspects uncovered.
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 appropriately sized and front-loaded: it starts with the core purpose, explains the rationale, specifies the return values, and provides usage instructions and parameter details in a structured way. Every sentence adds value without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, no annotations, but with an output schema), the description is complete enough. It covers purpose, usage, behavior, and parameters, and since an output schema exists, it doesn't need to explain return values in depth. This provides adequate context for an AI agent to select and invoke the tool correctly.
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 schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for the single parameter 'text' by explaining it as 'Text to analyze (typically agent output)', which clarifies usage context beyond the bare schema. Since there are no other parameters, this is sufficient, but it doesn't detail format constraints or examples, keeping it from a perfect score.
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 tool's purpose with specific verbs ('detects weak quantifiers') and resources ('text to analyze'), and distinguishes it from siblings by focusing on statistical evidence requirements for quantifiers like 'frequently', 'typically', 'always', 'never'. It explicitly mentions what the tool returns ('allow', 'require_count', or 'block'), making the purpose distinct and well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines: it specifies when to use ('on agent output BEFORE returning to user'), the context ('text to analyze, typically agent output'), and implies alternatives by highlighting its unique focus on weak quantifiers, which differentiates it from sibling tools like 'validate_claim' or 'detect_inference_violations'. This gives clear direction on application timing and scope.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates that this is an analysis tool (not a mutation) and specifies what it returns (detected languages, original text presence, etc.). However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions, leaving some behavioral aspects uncovered.
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 efficiently structured with a clear purpose statement, specific capabilities listed, critical usage guideline, and separate Args/Returns sections. Every sentence earns its place, with no redundant information, making it easy to parse while being comprehensive.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's analytical nature, single parameter, and presence of an output schema (which handles return value documentation), the description provides complete context. It covers purpose, usage prerequisites, parameter meaning, and behavioral expectations without needing to duplicate what the output schema will specify about the analysis results.
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 schema has 0% description coverage for its single parameter 'document_id'. The description compensates by explaining that this is 'ID of the document to analyze,' providing essential context about what this parameter represents. While it doesn't specify format constraints or examples, it adds meaningful semantic value beyond the bare schema.
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 explicitly states the tool's purpose: 'Analyze what a document CAN and CANNOT support' with specific details about detecting languages, original text presence, variant availability, and epistemological limitations. It clearly distinguishes this analysis tool from siblings like 'get_metadata' or 'get_epistemological_report' by focusing on source capabilities rather than general metadata or epistemological assessments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'MUST be called before making claims about morphology, etymology, or textual criticism.' This creates clear prerequisites for using other tools and distinguishes it from alternatives like 'validate_claim' or 'detect_text_genre' by establishing it as a foundational analysis step.
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?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it explains the three possible confidence outcomes ('textual', 'partial', 'not_found'), describes the fuzzy matching mechanism with threshold parameter context, and clarifies the preventive purpose against hallucination. It doesn't mention rate limits, authentication needs, or performance characteristics, but covers the core operational behavior adequately.
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 perfectly structured and concise. The first sentence states the core purpose, followed by usage timing, return values explanation, and preventive benefit. The Args and Returns sections are clearly labeled and provide essential information without redundancy. Every sentence earns its place with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, 1 required), no annotations, but with an output schema present, the description provides excellent contextual completeness. It covers purpose, usage timing, behavioral outcomes, parameter semantics, and the preventive rationale. The existence of an output schema means the description doesn't need to detail return structure, allowing it to focus on operational context, which it does comprehensively.
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?
With 0% schema description coverage, the description must compensate and does so effectively for most parameters. It explains 'quote' as 'exact quote to validate', clarifies that document_id and segment_id are optional search scopes, and provides meaningful context for 'fuzzy_threshold' as 'similarity threshold for partial matches (0-1)'. The description doesn't explicitly mention the default value for fuzzy_threshold or the null handling for optional parameters, but provides substantial semantic value beyond the bare schema.
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 tool's purpose with specific verbs ('verify', 'validate') and resources ('quoted string', 'segment or document'). It distinguishes itself from siblings by focusing on exact quote validation rather than broader analysis or search operations, with explicit mention of preventing 'pattern completion hallucination' which sets it apart from tools like 'search_segment' or 'validate_claim'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'Use BEFORE claiming any text appears in the source.' This creates clear context for application and distinguishes it from reactive validation tools. While it doesn't name specific alternatives, the 'before' timing guidance effectively positions this as a preventive measure against hallucination.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs a validation check based on distance constraints, returns a result, and implies it's a read-only operation (no destructive effects mentioned). However, it doesn't specify error handling, performance characteristics, or authentication needs, leaving some gaps 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by usage guidelines, and then parameter and return details in a structured format. Every sentence earns its place by adding value, with no redundant or vague phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (validation with three parameters), no annotations, and the presence of an output schema (which handles return values), the description is complete. It covers purpose, usage, parameters, and behavioral context adequately, leaving no significant gaps for an AI agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate fully. It does so by explaining all three parameters: 'base_segment_id' as the anchor, 'target_segment_id' as the referenced segment, and 'max_distance' with semantics (0 = same, 1 = adjacent) and a default value. This adds crucial meaning beyond the bare schema, making parameter usage clear.
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 tool's purpose with specific verbs ('check if two segments are adjacent') and resources ('segments'), distinguishing it from siblings like 'get_adjacent_segments' (which retrieves segments) or 'compare_segments' (which likely compares content). It explicitly mentions 'within allowed distance' and 'enforce constraints,' making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use to enforce "same verse or verse+1" constraints' and 'Prevents narrative jump violations.' This clearly indicates when to use this tool (for proximity validation in narrative contexts) and implies alternatives are not needed for this specific constraint-checking task, given the sibling tools focus on other validations like 'validate_claim' or 'validate_literal_quote.'
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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