OpenPapers

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation2/5
The extract_training_parameters, extract_training_recipe, and extract_training_recipe_from_url tools are nearly interchangeable, and extract_paper_facts vs extract_paper_claims can be hard to distinguish without careful reading. Other compare/search tools are mostly separated by their descriptions, but these overlaps make tool selection risky.
Naming Consistency4/5
Most tools follow a clear verb_noun pattern such as search_papers, get_citations, and add_paper_to_collection, with related operations sharing prefixes like find_, extract_, and compare_. Minor deviations like vector_search and search_within_paper prevent a perfect score.
Tool Count2/5
36 tools is well beyond the 25+ threshold and creates significant cognitive overhead, even though the domain is broad. The set could be pruned by merging the training extraction variants and reducing repository/config inspection granularity.
Completeness4/5
The surface covers core scholarly workflows: discovery, reading, citation graphs, extraction, comparison, collections, refresh, and ResearchPack export/import. Minor gaps exist around managing persisted claims and direct collection retrieval, but these are workable.
Average 3.2/5 across 34 of 36 tools scored. Lowest: 2.2/5.
See the Tool Scores section below for per-tool breakdowns.
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}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Retrieve' implies a read operation, but the description does not disclose any behavioral details such as response format, pagination, rate limits, error behavior, or what 'provenance' means in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and to the point, with no wasted words. However, the brevity borders on under-specification, and the phrase 'with provenance' is unexplained, so the structure is not as effective as it could be.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and a 0% parameter description coverage, this description is too thin to let an agent correctly invoke the tool. It lacks parameter semantics, expected output, and relationship to sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description says nothing about paper_id or limit. The schema provides only type/default/min/max constraints, leaving the agent without any explanation of what each parameter means or how they affect the request.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific action and resource: 'Retrieve Semantic Scholar recommendations with provenance.' This is more specific than a mere restatement, but it does not distinguish the tool from related sibling tools like get_references or get_citations, and 'provenance' is vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives. With 35 sibling tools including several retrieval-related options, the absence of any selection criteria leaves the agent to infer the appropriate use case.
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 disclosing behavior. It indicates the tool returns a profile and linked paper identifiers, but it does not explain what 'resolve' means, whether it performs a lookup or mapping, what input formats are accepted, or what failure modes exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler and the main resource is front-loaded. It is appropriately short for a simple one-parameter tool, though it sacrifices useful detail for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no annotations and no output schema, so the description must compensate by explaining inputs, behavior, and return values. It only states the high-level outcome, leaving important usage details undocumented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the meaning or expected format of author_id. The schema only states it is a non-empty string, leaving the agent without critical information about what identifier to supply.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('resolve') and names a concrete resource ('Semantic Scholar author profile and linked paper identifiers'). It is distinct from all sibling tools, none of which target author resolution, so an agent can identify the intended purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives, and no mention of prerequisites or context. The description implies 'use this when you need author info,' but it does not state exclusions, fallbacks, or comparisons with 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, the description carries the full burden of behavioral disclosure, but it only gestures at behavior through the unexplained 'with provenance'. It does not state what happens for an invalid or missing paper_id, what provenance covers, whether results are sorted or paginated, or anything about the response shape.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with the verb front-loaded and no filler. It earns most of its words, though the trailing 'with provenance' adds little since it is never elaborated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and zero parameter descriptions, the tool description should carry substantial context but does not. It omits return-value expectations, parameter semantics, and sibling differentiation — an agent is left guessing about how to invoke it correctly and what to do with the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, but it adds only the context that the tool deals with Semantic Scholar papers. It fails to clarify the expected paper_id format (e.g., S2 corpus ID vs arXiv ID) or what the limit parameter controls beyond the numeric bounds already present in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Retrieve') and a concrete resource ('Semantic Scholar references'), so the core action is clear. However, it does not differentiate this tool from the sibling get_citations, and the phrase 'with provenance' is vague — an agent cannot tell what provenance data is actually included.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use get_references versus its many siblings, especially the closely related get_citations, get_related_papers, and get_paper. No conditions, exclusions, or alternative tool names are given, so the agent must guess at the intended selection criteria.
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 reveals reliance on a configured vector backend and an indexed corpus, but does not explain return format, ranking behavior, error cases, or what happens when the backend is missing or unconfigured.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with no filler and the core action front-loaded. It is concise, though its brevity contributes to under-specification in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and many similar sibling tools, the description is not complete enough. An agent cannot confidently determine when to choose this over other search tools, what results to expect, or what assumptions about the vector backend are safe.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for parameter meaning, but it does not. It never clarifies how 'query' should be interpreted (e.g., natural language, semantic text) or what 'limit' controls beyond the schema's basic definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Search') and resource ('indexed paper vectors'), making the core purpose clear. The mention of a 'configured vector backend' hints at semantic/vector search and loosely distinguishes it from keyword-style tools like search_papers, though it does not explicitly name an alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus search_papers or other sibling tools. The description implies a vector-based search but does not state when that is preferred, when it is not, or what distinguishes it from alternative search 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. It does disclose that the tool persists data and reports conflicts, but it does not explain what persistence means, whether existing claims are overwritten or duplicated, what side effects occur, or what the response looks like. This is a mutation-like operation with insufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler or repetition. Every phrase adds meaning: 'persist', 'heuristic paper claims', and 'report explicit conflicts with prior claims' all contribute. It is front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although the tool is simple (one parameter, no output schema), the description does not complete the picture for an agent. It omits what 'claims' means here, how a paper is obtained from the url, what 'explicit conflicts' look like, and what output or confirmation is returned. The lack of annotations and output schema makes these gaps more significant.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter, url, with 0% description coverage, and the tool description never mentions the url parameter. The schema's name, format, and maxLength provide some clarity, but the description does not explain how the url relates to extracting or persisting claims, leaving the parameter semantics incomplete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific action ('Persist heuristic paper claims') and a clear resource ('paper claims'), and it differentiates from related siblings like extract_paper_facts by focusing on claims and reporting conflicts. The verb 'persist' slightly mismatches the tool name's 'extract', but the overall purpose is still decipherable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance about when to use this tool versus alternatives such as extract_paper_facts, verify_claim, or compare_papers. There is no explicit when-to-use, when-not-to-use, or alternative routing, so an agent must infer usage from the tool name and sibling list.
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, the description bears the full burden. It does disclose a meaningful behavioral guarantee: benchmark comparability is kept explicit when evidence is missing, suggesting the tool avoids silent inference. However, it does not describe side effects, return behavior, or how missing metadata is surfaced beyond that one clause.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded with the core action and resource. The second sentence is somewhat jargon-cryptic but adds a behavioral nuance without added length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and 0% schema coverage, the description leaves important context missing: what a comparison result looks like, when to choose this over the other compare tools, and how to interpret the benchmark-comparability behavior. The agent can identify the basic operation but not fully route or use it confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needed to compensate. It adds the idea of 'paper metadata' but says nothing about the left/right identification semantics, ID format, or relationship between the two parameters beyond their self-explanatory names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a concrete action ('Compare') on a specific resource ('verified local paper metadata'), which distinguishes it from sibling comparison tools aimed at methods or code. The second clause about benchmark comparability is obscure but does not obscure the core purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus siblings such as compare_methods or compare_paper_to_code. The qualifiers 'verified local' imply a scope restriction, but no exclusions or selection criteria are 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?
With no annotations, the description must carry the full behavioral disclosure burden. It indicates local metadata is modified ('Refresh locally stored metadata') but does not disclose potential overwrites, network dependence, failure behavior when no provider-native identifier is available, or whether remote data is affected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence with no filler. It front-loads the action and object, though the phrase 'available provider-native identifier' is somewhat dense and could be clearer.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter refresh action, the description covers the core operation and parameter semantics adequately. However, it omits expected output, edge cases like a missing provider-native identifier, and any caveats about modifying local state, leaving some room for agent confusion.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only the parameter name and basic string constraints with 0% description coverage. The description adds meaning by identifying paper_id as a provider-native identifier, which helps the agent understand what value to supply, but it does not explain what 'provider-native' means or how to obtain such an identifier.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Refresh') and resource ('locally stored metadata') and adds the mechanism ('provider-native identifier'). It distinguishes the target from the sibling refresh_collection tool by identifying the paper-specific scope, though it does not explicitly contrast with any sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives like get_paper, search_papers, or refresh_collection. The description implies a use case but provides no explicit conditions, prerequisites, or exclusions.
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, the description carries the full burden. It does disclose a read-oriented search behavior and hints at output traits ('provider transparency and evidence'), which adds some context. However, it does not explain what 'verified literature' means, whether results are curated, or any limitations or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence with the main action front-loaded, and there is no filler or repetition. It is concise, though the compressed phrase 'with provider transparency and evidence' could be clearer.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and many closely related sibling tools, the description is too sparse. It does not explain return value shape, the meaning of 'verified', the 'limit' parameter, or how this tool relates to the many other search/compare tools in the family.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It clearly implies the meaning of 'method' as a named method to search for, but it does not explain the 'limit' parameter at all, leaving one of the two parameters semantically undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Search'), a resource ('verified literature'), and a target ('a named method'), which is clear enough to differentiate it from generic paper search. However, it does not explicitly distinguish it from siblings like search_papers or vector_search, and the phrase 'provider transparency and evidence' is vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no explicit guidance about when to use this tool versus alternatives such as search_papers, research_topic, compare_methods, or vector_search. The intended use case is only implied by the wording, with no when-to-use or when-not-to-use direction.
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, the description carries the full behavioral disclosure burden. It does disclose meaningful output behavior: 'Synthesis is labeled; no unsupported claims are emitted.' However, it does not mention whether the tool performs searches, whether it has side effects, how sources are selected, or what the returned overview contains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise: two short sentences, with the primary action front-loaded in the first sentence. Every phrase adds meaningful information about the tool's purpose or behavioral constraints, with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given four parameters, two enums, no output schema, and no annotations, the description is too sparse to support correct invocation. An agent cannot tell what 'depth' or 'objective' control, what the final overview looks like, or how the provenance guarantee is reflected in the output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining at least the key parameters. It does not mention topic, depth, limit, or objective at all, leaving the agent to infer meaning solely from parameter names and enum values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action and deliverable: 'Build a provenance-preserving literature overview.' It also adds a distinctive qualifier ('provenance-preserving') that helps set it apart from generic research tools, though it does not explicitly name sibling tools or contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives like research_method or build_research_report. It implies a literature-overview use case, but provides no explicit conditions, exclusions, or references to sibling tools.
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 does disclose useful behavioral traits: the extraction is deterministic, facts have source locators, and unsupported values are not inferred. However, it does not explain side effects, prerequisites, URL handling, or failure behavior, which leaves some important transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It front-loads the core action and constraint, and the second clause adds important behavioral nuance without bloating the text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the simple one-parameter schema, the description is not complete enough given the crowded sibling toolset. It does not explain what counts as a 'fact', what the output looks like beyond source locators, or how this differs from extract_paper_claims. With no output schema and no annotations, the description needs to carry more weight.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description never mentions the `url` parameter. The parameter's purpose can only be inferred from the property name and the tool name, not from the description itself. The description should clarify that the URL points to the paper to analyze, but it does not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific operation ('Extract deterministic heuristic facts') and adds a defining constraint ('with source locators; no unsupported values are inferred'). This is clearer than a tautology, but it does not explicitly differentiate from the many sibling extraction tools like extract_paper_claims or extract_training_parameters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not say when to prefer this tool over extract_paper_claims, verify_claim, or other extraction-focused siblings, nor does it state conditions or exclusions. The only hint is the phrase 'deterministic heuristic facts', which is too weak to route an agent confidently.
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 behavioral burden. It usefully discloses that only explicitly labeled values are extracted and that source locators are included in output. However, it does not describe behavior when no labeled values exist, whether the URL is fetched, or any side effects, though 'extract' implies a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tight sentence with no filler. It front-loads the action ('Extract') and packs the key constraints ('only explicitly labeled', 'with source locators') into a compact structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple one-parameter tool, but with no annotations and no output schema, the description should do more to explain what counts as a training value, what a source locator looks like, and how the URL relates to the source. The tool is probably callable, but an agent would be guessing about return structure and exact input expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needs to clarify the url parameter. It does not state that the URL points to the paper or document from which training values should be extracted, nor does it explain what kind of URL is expected. The property name and format uri make this somewhat inferable, but the description adds no direct parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action—extract—and a specific resource: only explicitly labeled training values, with source locators delivered. It is distinguishable from sibling tools like extract_training_recipe or extract_paper_claims because it emphasizes labeled values and locators, but 'training values' is somewhat vague and the source is not named explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives such as extract_training_recipe, extract_training_recipe_from_url, or extract_paper_facts. The description only states what the tool does, not when it should be preferred or when it should be avoided.
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. It does disclose that status is UNKNOWN unless separately verified, which is a useful caveat, but it does not explain what 'status' means, what output to expect, or whether any verification happens implicitly. The read-only nature is implied by 'find' but not stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The core action is front-loaded and the verification-status caveat is a meaningful, compact addition. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and 0% parameter coverage, the description is too thin. It omits expected output structure, the meaning of 'status,' how verification is determined, and practical information about parameter usage. An agent would need to guess at important calling details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does 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 mentions 'paper or method,' mapping to paper_id and method, but it does not explain the limit parameter, whether at least one of method/paper_id is required, or how the two interact. This is only partial compensation for the missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds GitHub repositories related to a verified paper or method, which is a specific verb and resource. It is distinguishable from siblings like find_models and find_datasets by the explicit 'GitHub repositories' target, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'verified paper or method' implies the tool is for verified items and the status caveat suggests verification matters, but no explicit when-to-use or when-not-to-use guidance is provided. It does not mention alternatives such as find_models or find_datasets.
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?
The description explicitly discloses a key behavioral guarantee: repository content is never executed. However, with no annotations, it does not cover other important traits such as return format, authentication requirements, rate limits, or how missing revisions are handled.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no wasted words. The most important safety-related behavior is front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with four parameters, no annotations, and no output schema, the description is too sparse. It does not explain what the returned data looks like, how path should be interpreted, or what happens when ref is omitted, leaving meaningful gaps for correct use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does 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 hints at the optional revision via 'at an optional revision', and owner, repo, and path are left entirely implicit. This is minimal added meaning beyond the schema field names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Read a repository file') and adds a distinguishing qualifier ('statically', 'never executed'). It is clear about the resource, though it says 'repository file' rather than 'repository config', leaving slight ambiguity about scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. With many siblings including find_repository_configs, the description should clarify when to read a specific file versus search for configs, but it does not.
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 and does disclose some behavioral detail: it searches parsed chunks and returns matches with source locations. However, it does not explain whether the search is lexical or semantic, how chunking affects results, what 'source-located' means in practice, or whether the operation is side-effect free.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that front-loads the core action and output. There is no redundant wording, filler, or repetition of the tool name/title. It earns its place by conveying the essential behavior efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with three parameters, no output schema, and no annotations, the description is too thin to fully guide an agent. It omits how the URL is used, what the query should look like, how limit applies, what format the source-located matches take, and what happens when the paper cannot be found or parsed. Basic purpose is clear, but an agent would still have to infer several important call details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the lack of parameter documentation, but it does not mention url, query, or limit at all. The parameter names and constraints are somewhat self-explanatory, especially 'query' and 'limit', but the description adds no meaning about how these parameters interact with 'parsed paper chunks' or 'source-located matches'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action ('Search') and the target resource ('parsed paper chunks'), while 'source-located matches' adds a concrete output expectation. The phrase 'within_paper' in the name and 'parsed paper chunks' in the description help distinguish it from broader search tools, though it does not explicitly name or contrast any sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus alternatives like search_papers or vector_search. The description implies use on a specific paper via 'within paper' and 'parsed paper chunks', but it never states conditions, exclusions, or when a different tool would be more appropriate.
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, the description carries the behavioral burden. It discloses that the collection is persistent and local, and that the tool may either create or retrieve, which implies an idempotent side-effect profile. However, it does not state what happens if the collection already exists, what is returned, or any error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one sentence with no wasted words. The action is front-loaded, and the qualifiers 'persistent local' and 'or retrieve' earn their place by adding behavioral context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema and no annotations, the description gives the core semantics of create/retrieve and persistence, which is adequate but still leaves gaps. It does not explain the return value, duplicate-name handling, or how the name parameter maps to identity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, and the description does not mention the sole required parameter 'name' at all. The parameter is inferable from the tool name and resource type, but the description adds no explicit meaning beyond the schema's type and length constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Create or retrieve') and a specific resource ('persistent local paper collection'). The 'or retrieve' qualifier signals idempotent behavior, which adds useful specificity, though it does not fully distinguish the tool from list_collections, which also retrieves collections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives such as list_collections or add_paper_to_collection. The phrase 'or retrieve' hints at an upsert-style use case, but there is no clear statement of when this tool should be chosen over siblings.
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 provides a meaningful guarantee against hallucination ('Missing values are never guessed') and clarifies that only source-backed values are returned, but it does not explain what happens when no values exist, whether it returns partial data, or what error/edge-case behavior to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two short sentences with no wasted words. It front-loads the core function ('Return only source-backed training parameters') and then adds the key constraint about not guessing missing values.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema and no annotations, the description defines the basic return contract but leaves gaps. It does not define what counts as a 'training recipe', nor does it distinguish itself from 'extract_training_parameters', making selection among siblings less reliable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description should compensate for the parameter documentation. It does not mention 'paper_id' at all or explain how it should be formatted, though the parameter name is reasonably self-explanatory.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Return'), a clear resource ('source-backed training parameters'), and a behavioral constraint ('Missing values are never guessed'). However, it does not differentiate from the sibling tool 'extract_training_parameters', which likely performs a very similar function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or the relationship to similar tools like 'extract_training_parameters' or 'extract_training_recipe_from_url'.
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?
There are no annotations, so the description carries the behavioral disclosure burden. It does provide a useful safety-relevant trait by stating the data is treated as untrusted static metadata and that no code is executed. However, it omits what happens after import, whether the tool mutates or replaces state, and what response or error behavior to expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly worded sentence. It front-loads the core action and includes an important security-relevant qualifier without any filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with one parameter and no output schema, so a short description is acceptable. Still, without annotations or return-value details, the agent is left uncertain about the tool's state-changing behavior and post-import effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description only adds that pack_json is a bounded JSON ResearchPack. It does not explain the expected JSON structure, required fields, or any formatting constraints beyond the schema's maxLength/minLength, leaving the agent under-informed about valid input.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action (Import) and resource (ResearchPack), and specifies the input as a bounded JSON. It does not explicitly name sibling tools, but the wording distinguishes it from export_research_pack and the various search/read tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool instead of alternatives, such as export_research_pack or collection tools. No scenarios, prerequisites, or exclusions are mentioned, so the agent must infer appropriate use from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'acquire' (implying downloading) and 'bounded' (implying some size or scope limit), but it does not describe output format, failure modes, authentication needs, or what happens with non-HTML or unbounded documents.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence with no filler and gets to the point immediately. It is appropriately front-loaded, though the brevity comes at the cost of omitting important usage and output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one required parameter and no output schema, the description should state what the returned parsed result looks like or at least what an agent can do with it. It only vaguely gestures at 'section and reference boundaries' without explaining the return structure, limits, or when this tool is preferable to siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero description coverage, so the description must compensate. 'HTML paper document' implies the url parameter should point to an HTML paper, and 'bounded' hints at size constraints. However, it never explicitly names url or explains what 'bounded' means in practical terms.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs ('Acquire and parse') and a specific resource ('bounded HTML paper document') with clear section and reference boundaries. This distinguishes it from sibling tools like get_paper or search_within_paper by indicating a structured parse operation rather than simple retrieval or search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or how it differs from get_paper, search_papers, or extract_paper_facts. The usage context is only implied by the title and name, not stated.
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 and does add useful behavioral context by saying the report is 'cited' and that facts and recommendations are 'separated.' It does not, however, disclose side effects, required permissions, latency, or what happens on failure, which would be valuable for a report-building operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler, and it front-loads the action and target artifact. Every phrase earns its place, and the key output trait is included without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 0% parameter coverage, the description is not complete enough for an agent to invoke the tool correctly in varied situations. It omits query semantics, limit behavior, output format beyond facts/recommendations, and how this relates to the many sibling research tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, but it only partially does. The phrase 'literature, implementation, or reproducibility' maps to the mode enum values, but query and limit are not explained at all, leaving two of three parameters semantically underdocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Build') and resource ('cited literature, implementation, or reproducibility report') with a clear output characteristic ('facts and recommendations separated'). It is clear about what the tool produces, though it does not explicitly contrast itself with similar siblings like research_topic or research_method.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The three report types imply when the tool should be used, and the mention of citations and separated facts/recommendations gives some context. However, there is no explicit guidance about when to prefer this tool over siblings, nor any exclusions or prerequisite conditions.
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, the description carries the full behavioral burden. It adds one meaningful behavioral constraint: 'only explicitly reported' fields are populated, which tells the agent not to infer missing values. However, it does not mention side effects, fetching behavior, failure modes, or return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler. The key behavioral qualifier 'only explicitly reported' is front-loaded, followed immediately by the input source. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, with one parameter and no output schema, so a short description is acceptable. However, it omits any indication of what fields constitute a training recipe, what the return value looks like, and how it relates to sibling extraction tools. It is adequate for basic selection but not fully enough for confident invocation in all cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides the 'url' parameter with format 'uri' and maxLength, but no description. The tool description adds the important semantic qualifier that it is a 'paper URL'. This is useful but minimal; it does not fully compensate for the 0% schema description coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action ('populate') and resource ('training recipe fields') and clarifies the input source ('a paper URL'). It is clear and non-tautological, but it does not explicitly distinguish itself from the sibling tools 'extract_training_recipe' or 'extract_training_parameters' beyond the URL-based name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance about when to use this tool versus alternatives like 'extract_training_recipe' or 'extract_training_parameters'. The phrase 'from a paper URL' implies the input context, but the description does not state exclusions, prerequisites, or decision boundaries.
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?
There are no annotations, so the description carries the behavioral burden. It indicates a read-only search operation and discloses that revision and card metadata are returned when available. It does not cover pagination, ordering, network/auth requirements, or failure behavior, but for a simple search tool the main behavior is clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single tight sentence that front-loads the core purpose and then adds a useful output detail. There is no fluff or redundant restating of the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter search tool without an output schema, the description is adequate but not rich. It names the resource and hints at returned metadata, but it does not explain query behavior, result shape, or how limit affects output. Given the minimal schema, more detail would meaningfully improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining the query and limit parameters. It does not mention either parameter explicitly. The parameter names and schema constraints provide some meaning, but the description adds no real semantic value beyond what can be inferred from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds Hugging Face model repositories and mentions the metadata content it returns. It is distinguishable from sibling tools like find_datasets and find_implementations, though it does not explicitly name alternatives or edge cases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for searching model repositories on Hugging Face, which provides reasonable context. However, it does not explicitly state when to use this tool over similar siblings like find_datasets or find_repository_configs, nor does it provide any exclusion criteria.
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 burden. It does disclose that results include provenance and transparent ranking, which is useful behavioral context. However, it does not explain the meaning of 'verified,' result formatting, pagination, or any limitations, so it only partially satisfies this dimension.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short, front-loaded sentences. Every clause adds information: the action, the source scope, the verification, and the output character. No filler or redundant restatement of the name/title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 7-parameter tool with no output schema and no annotations, the description is too thin. It does not specify how filters interact, what the result object looks like, or what constraints apply to cross-source metadata search, so an agent cannot reliably craft valid calls beyond the required query.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the meaning or usage of limit, topic, venue, author, year_from, or year_to. The phrase 'across arXiv and Crossref' gives some context for the query parameter, but the other six parameters are left entirely to the agent's inference.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific action (Search), a concrete resource (verified scholarly metadata), and the data sources (arXiv and Crossref). This is enough to distinguish it from siblings like vector_search, search_within_paper, or get_paper, which operate on different scopes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to choose this tool over search_within_paper, vector_search, or get_related_papers. The description neither names alternatives nor states exclusions or prerequisites, leaving the agent to infer the appropriate 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?
The description discloses one important behavioral trait: it does not upgrade derived evidence. However, with no annotations provided, the description carries the full burden, and it does not clarify whether the operation is read-only, what side effects may occur, or what 'resolve' entails beyond reporting status.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with no filler. Every phrase contributes meaning, and the behavioral caveat is incorporated without bloating the description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description is adequate but incomplete: it does not define what 'evidence status' is, what the response contains, or whether the claim must already exist. With no annotations or output schema, more context about expected results and failure modes would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The one required parameter, claim_id, is not described in the schema. The description partially compensates by tying it to 'a persisted claim,' but it does not explain how to obtain a claim_id or what kinds of claims are valid inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific action (resolve/report) on a specific resource (persisted claim) and states the outcome (current evidence status). The phrase 'without upgrading derived evidence' adds useful scope, though 'Resolve' is slightly ambiguous and it does not explicitly distinguish from sibling claim-related tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives such as extract_paper_claims or refresh_paper. The description does not mention prerequisites, exclusions, or which sibling tools should be chosen instead under different conditions.
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 explaining behavior. It states that the operation is a retrieval, but does not disclose what exactly is returned, how 'canonical' is determined, error behavior for unknown IDs, or any other runtime details. This is a significant gap for a standalone tool definition.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence. Every word contributes: the verb, the resource type, and the acceptable identifier formats. There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter retrieval tool, the description is minimally viable. It defines the core action and the input, but without an output schema or annotations it does not explain the return value, which is a notable gap for an agent deciding whether to use this tool instead of get_bibtex, read_paper, or search_papers.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With only one parameter and 0% schema description coverage, the description compensates well by clarifying that paper_id can be a paper ID, DOI, or arXiv ID. This gives the agent meaningful guidance beyond the plain string type in the schema, though exact format examples are not provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('retrieve') and a clear resource ('canonical research work'), and specifies the identifier types accepted. It is not a tautology and provides enough detail to distinguish it from search-oriented siblings, though it does not name them explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: when you already have a paper ID, DOI, or arXiv ID. However, it does not explicitly say when not to use it or point to alternatives like search_papers or read_paper, leaving some inference to the agent.
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 exist, so the description carries the full behavioral burden. It discloses one genuinely useful boundary — the tool reports verified overlap and explicitly does not infer benchmark equivalence — which shapes downstream agent reasoning. It leaves the read-only status, rate or volume behavior, and zero-overlap handling undisclosed, so the disclosure is partial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence with the verb front-loaded and every clause earning its place: inputs ('two bounded method searches'), output ('verified paper overlap'), and a scope boundary ('without inferring benchmark equivalence'). There is no filler, repetition, or schema echo.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description must convey the return shape, and 'report verified paper overlap' only hints at it — an agent cannot tell whether the result is a list of paper identifiers, a count, or detailed records. The ambiguous 'bounded' qualifier and the unexplained limit parameter add further gaps that nothing else in the definition fills, since there are no annotations and no schema descriptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does 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 maps the two required string arguments to 'two bounded method searches,' which orients the agent, but it never states what makes a search 'bounded,' how that relates to the limit parameter, or what limit (default 10, max 50) actually controls. The compensation is partial at best and leaves the limit parameter's meaning to inference.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action ('Compare'), a specific resource ('two bounded method searches'), and a specific output ('verified paper overlap'). It differentiates from siblings like compare_papers and compare_paper_to_code by scoping to method-search results, and the closing qualifier 'without inferring benchmark equivalence' further disambiguates what the tool does not claim. An agent can tell this apart from its comparison siblings without reading their schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use case is implied: call this when you have two bounded method searches and want to know their paper overlap. However, no alternative tools are named and there is no when-not guidance, which matters because compare_papers and compare_paper_to_code create real routing ambiguity among siblings. The 'without inferring benchmark equivalence' clause is the closest thing to an exclusion, but it is framed as a scope statement rather than usage 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, the description carries the behavioral burden. It clearly indicates a read-only retrieval operation and adds that results include provenance, but it does not mention pagination, ordering, potential absent data, or how provenance is represented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler. Every word adds meaning: the verb, the resource, the source, and the provenance qualifier.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter retrieval tool, the description plus schema covers the essentials: what is returned (citing works with provenance), the required identifier, and the optional limit with bounds. The output shape is not detailed, but none is expected for this lightweight call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain paper_id or limit. Although the parameter names are intuitive, the description does not compensate for the lack of schema documentation, especially the meaning of paper_id as the paper whose citing works are returned.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific operation ('Retrieve') and resource ('Semantic Scholar citing works'), and the phrase 'citing works' distinguishes it from the sibling get_references (cited works). The addition 'with provenance' further clarifies the scope of returned data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to choose this tool over closely related siblings such as get_references or get_related_papers. The agent must infer that this is for inbound citations rather than outbound references or related works.
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, the description carries the behavioral disclosure burden and does add some signal: the collection is persistent and local, and the paper must already exist and be verified. However, it leaves undiscussed mutation-related behavior such as duplicate handling, idempotency, failure when the collection does not exist, and access requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence with no filler or repetition of the title beyond the necessary verb and object. Every phrase earns its place by adding a relevant qualifier: existing, verified, persistent, local.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool with two required string IDs, no output schema, and no enums/nesting, the description provides sufficient context to select and invoke the operation. The main omission is guidance about edge cases, but that is less critical given the simple add-to-collection scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema shows only paper_id and collection_id strings with no descriptions (0% coverage), so the description must compensate. It does indirectly: paper_id refers to an existing verified paper and collection_id refers to a persistent local collection. It does not relate the parameters with explicit field names, but the tool name and sentence make the mapping recoverable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Add'), an object ('existing verified paper'), and a destination ('persistent local collection'), so the core operation is unmistakable. It is implicitly distinct from sibling operations like removing, creating, or verifying, though it does not explicitly contrast itself with any sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description only implies when to use it: when a user has an already verified paper and wants to place it into a persistent local collection. It does not name alternatives such as create_collection for missing collections or remove_paper_from_collection for removals, nor does it state explicit when-not-to-use conditions.
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, the description must carry the behavioral disclosure burden. It usefully states what results include (revision, card metadata, reconciled paper links) and notes that paper links are conditional ('when available'). However, it does not describe search semantics, result ordering, pagination, or output shape, leaving moderate gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, focused sentence that places the verb and target resource first, then adds useful qualifiers. No unnecessary words or redundant restatements of the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter search tool, the description covers the core purpose and some result details, but it omits guidance on when to choose this tool over related finders and leaves parameter semantics undocumented. It is adequate but not fully self-sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. It does not explain what 'query' should contain or how 'limit' affects results. While the parameter names are somewhat self-explanatory, the description adds no specific meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Find'), a specific resource ('dataset repositories on Hugging Face'), and adds distinctive result details ('revision, card metadata, and reconciled paper links'). This clearly distinguishes it from sibling tools like find_models and find_implementations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching Hugging Face dataset repositories, but it does not explicitly state when to use this tool over alternatives such as find_models or search_papers. No exclusions or selection criteria 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, the description carries the burden of behavioral disclosure and does a solid job: it explicitly states that no code execution occurs and no recursive crawling happens, which are important safety and cost characteristics. It does not describe output shape or limits, but the core non-destructive, bounded behavior is clearly disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence with no filler; the primary action, object, and key exclusions are all present and front-loaded. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a simple list operation but lacks usage context, alternative routing, and parameter details. With no output schema and no annotations, this leaves some gaps, though the scope itself is clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, but it only hints at 'repository revision' (ref) and 'repository' (owner/repo) without explaining formats, defaults, or relationships. The agent gets little beyond the schema's basic names and patterns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and a specific resource ('common configuration files at a repository revision'), and clarifies what it does not do ('without executing or recursively crawling repository content'). This distinguishes it from content-search siblings like find_implementations and from the singular get_repository_config.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no when-to-use guidance and never mentions alternatives such as get_repository_config. It states the operation but leaves the agent to infer when this tool is the right choice versus a sibling.
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 needs to carry the behavioral burden, and it discloses a key safety trait: 'repository content is never executed.' It also signals that the comparison uses revision-pinned configuration, but it does not describe output, errors, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One tight sentence with no filler; the main purpose is front-loaded and the safety qualifier is appended without distracting. It earns every word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with four parameters (three required), nested objects, no output schema, and no annotations, a single high-level sentence is insufficient. The agent is not told what the comparison returns, what fields represents, or what conditions must be met before calling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema itself has 0% description coverage and no parameter descriptions, so the description must compensate; it only loosely maps to recipe and repository configuration. It does not explain the required fields array, its lineStart/lineEnd semantics, or the roles of source_url and commit_sha.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action ('Compare') and targets two distinct resources: a paper recipe and a repository configuration. It clearly distinguishes this tool from siblings like compare_papers or extract_training_recipe by framing the comparison as paper-vs-code.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The context that the user should have an explicit recipe and an extracted, revision-pinned repository configuration is clear, but there is no explicit when-to-use/when-not-to-use guidance or reference to alternative tools. The appropriate usage is implied rather than stated.
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 does add genuine value by stating 'deterministic' and 'without remote calls,' which are meaningful behavioral guarantees. But it omits other important behavior such as output format, side effects, whether a file is created, and permissions requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler. Key qualifiers like 'deterministic' and 'without remote calls' are included directly, and the structure front-loads the core behavior. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and no nested objects, but it also has no output schema and no annotations. The description does not explain what a ResearchPack is, what the return value will be, whether a file is written locally, or what failure modes exist. For a simple export tool, this is a moderate gap, not a fatal one.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needed to explain collection_id. It only refers to 'collection' generically; the parameter's role as the identifier of the collection to export is left largely to inference from the name and tool context. The schema only gives type and length constraints, so an agent can guess correctly, but the description adds little direct parameter guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a concrete action (export), a specific resource (a local collection ResearchPack), and two defining constraints: deterministic and no remote calls. This distinguishes it from siblings like import_research_pack and refresh_collection, which involve different operations or network behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'without remote calls' implies this tool is for offline/deterministic exports, and 'local collection' hints at the intended scope. However, it never explicitly states when to use this tool over alternatives, nor does it name a sibling as the better choice for remote-dependent or non-deterministic exports.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full transparency burden. It explicitly discloses a key behavioral trait: it will not fabricate metadata that is absent, and it returns canonical BibTeX. This is meaningful beyond the schema, though it does not address potential errors or exact output formatting.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence with no filler. The core action is front-loaded, and the behavioral qualifier is compact yet informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one required parameter and no output schema, the description is largely sufficient: it names the return value and a key behavioral guarantee. Minor gaps remain around identifier format and usage guidance, but the low complexity means these are not critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention paper_id at all. The property name is self-explanatory at a basic level, but the description adds no information about what kind of identifier is expected, such as an internal ID, DOI, or arXiv ID.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Return') with a specific resource ('canonical BibTeX') and adds a meaningful qualifier about not fabricating absent metadata. This clearly distinguishes it from sibling tools like get_paper or export_research_pack, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance about when to choose this tool over alternatives, nor does it mention any exclusions or prerequisites. The intended use is only implied by the tool name and the phrase 'Return canonical BibTeX,' with no explicit routing among the many sibling tools.
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, the description must carry behavioral disclosure, and it does state the key side-effect boundary: the paper is not deleted. It does not disclose other behaviors such as whether removal is idempotent, whether an empty collection remains, or what happens if the paper/collection does not exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The entire definition is one efficient sentence with no filler. The core action is front-loaded and the important 'without deleting the paper' caveat is included immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter mutation with no output schema, the description covers the essential semantics needed to invoke it: what is removed and what is preserved. It is not exhaustive (e.g., no error conditions or empty-collection behavior), but it is adequate for a low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needs to compensate. It names the conceptual objects (paper and collection) but does not explicitly map paper_id and collection_id to their roles or explain where valid IDs come from. The parameter names are quite self-explanatory, reducing the gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Remove') with a clear object ('a paper') and scope ('from a persistent local collection'), and distinguishes itself from deletion by adding 'without deleting the paper.' This clearly separates it from delete_collection and related paper-deletion tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the intended use: removing a paper's membership in a collection while preserving the paper itself. However, it does not explicitly state when to prefer this over add_paper_to_collection or delete_collection, or mention any prerequisites such as the collection existing.
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 transparency burden. It explicitly discloses that memberships are deleted as part of the operation and that papers are not deleted. It does not mention irreversibility or side effects, but the main destructive scope is clearly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of about 16 words, front-loaded with the action and resource, and contains no unnecessary filler. It earns its length and communicates the key distinction efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter delete operation with no output schema, the description is largely complete: it states what is deleted, what is preserved, and implies the intended use. It stops short of noting irreversibility or suggesting how to obtain the collection_id, so it is not a 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not add meaning about collection_id, such as where to find it or how it relates to the collection. The parameter name is self-descriptive and constrained, so it is not a 1, but the description provides no real guidance beyond the schema itself.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Delete') and resource ('persistent local collection'), and clearly defines the scope: collection and its memberships are removed, while papers are preserved. This distinguishes it from membership-only operations and paper deletion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for deleting an entire collection while retaining the underlying papers, but it does not explicitly name alternatives such as remove_paper_from_collection or state when not to use this tool. The usage context is clear but not fully explicit.
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 behavioral disclosure burden. It discloses that the operation targets locally stored metadata and preserves per-paper outcomes, which are non-obvious and useful traits. However, it omits details about permissions, whether remote data is fetched, failure behavior, and return value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. The main action is stated first, and the important preservation constraint is included concisely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter refresh operation, the description covers the core purpose and the key outcome-preservation behavior. But without an output schema or annotations, it leaves gaps around return/status information, error conditions, and when to choose refresh_paper instead of refresh_collection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one collection_id parameter with no descriptions and 0% schema coverage. The description mentions 'collection' and helps establish that the ID identifies a collection of papers, but it does not explain the parameter format, source, or how to obtain the ID.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action and resource: refreshing locally stored metadata for every paper in a collection. It also distinguishes itself from sibling refresh_paper by specifying collection-wide scope and adds an important qualifier about preserving per-paper outcomes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool: when metadata for all papers in a collection needs to be refreshed. It does not explicitly mention alternatives such as refresh_paper or state when not to use it, so it falls short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the disclosure burden. It adds useful behavioral context: collections are 'persistent' and 'local,' and the result includes 'memberships.' The verb 'List' semantically indicates a safe, read-only operation, so no destructive-behavior warning is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One short sentence with no filler. The verb and resource are front-loaded, and every word adds meaning: 'persistent,' 'local,' 'paper collections,' and 'memberships.'
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple zero-parameter listing tool with no output schema, the description is complete. It tells the agent what is listed, the scope of the data, and what the results will contain. No prerequisites, filters, or side effects are relevant.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and an empty input schema, so there is no parameter semantics for the description to clarify. Schema coverage is effectively complete, and no parameter guidance is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and clearly identifies the resource ('persistent local paper collections') plus what is included ('their memberships'). This distinguishes it from sibling tools like create_collection, delete_collection, and add_paper_to_collection without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies it should be used when the agent needs to see existing collections, but it does not explicitly name alternatives or state when not to use it. The intended use is reasonably inferable from the verb, but not explicitly contrasted with siblings.
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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