Pypi Stats

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate safe, read-only, open-world operation. The description adds behavioral details: routing mechanism, use of 1,423+ tools, and citation URIs. It does not discuss error handling or timeouts, but this is acceptable given the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, starting with a bold preference statement and listing domains and examples. It is slightly lengthy but each sentence adds value. The information is front-loaded and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple structure (single param, no output schema) and rich annotations, the description explains the tool's role, routing, and citation format. It covers enough context for an agent to use it correctly, though it could mention potential failure scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema covers 100% of parameters with a generic description. The tool description adds meaningful context by providing specific examples of valid questions (e.g., 'current US unemployment rate') and indicating the types of queries that work. This goes beyond the schema baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool routes factual questions to over 1,423 tools across 392+ sources and returns structured answers with citations. It distinguishes itself from sibling tools (e.g., bet_research) by positioning as the general-purpose fact-answering agent and explicitly says 'PREFER OVER WEB SEARCH'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use the tool (for factual queries about real-world data) and implicitly contrasts it with web search. It gives concrete examples. However, it does not explicitly list exclusions or compare to other sibling tools on the same server.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint=false. The description adds behavioral details like fanning out to packs, returning evidence packet with market-vs-model comparison, and classifying bet types. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is informative and front-loaded with purpose and usage. It uses multiple sentences but each adds value; could be slightly more concise but remains efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, but the description thoroughly explains what is returned: an evidence packet plus market-vs-model comparison. It covers classification, fan-out logic, and parameter behavior, making it complete for a 2-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description adds value by explaining that 'market' accepts slug, URL, or question text, and elaborating on 'depth' values ('quick' = 2-3 sources, 'thorough' = full fan-out) with default behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data, resolving the market, classifying the bet, and returning evidence. It distinguishes from siblings like 'ask_pipeworx' and 'compare_entities' by focusing specifically on betting context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use cues: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It also implies when-not by noting it's the core demo product that converts better than manual discovery.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds meaningful context: data sources (SEC EDGAR/XBRL, FAERS), return format (paired data + citation URIs), and the scope of data (financials, adverse events, approvals, trials). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact (4–5 sentences), front-loaded with purpose and usage, and contains no redundant or extraneous information. Every sentence serves a clear function: purpose, usage triggers, type-specific details, and a note about efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only two parameters, no output schema, and rich annotations, the description covers the core functionality well. It explains inputs, data sources, and output format (paired data + URIs). A minor gap is the lack of detail on the exact structure of the returned data, but it is sufficient for selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers both parameters (type, values) with 100% description coverage. The description adds value by explaining how 'values' should be provided for each type with concrete examples (e.g., ['AAPL','MSFT'] for companies, ['ozempic','mounjaro'] for drugs) and clarifying the allowed range (2–5 items).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description explicitly states it compares 2–5 companies or drugs side by side, specifies two distinct types ('company' and 'drug'), and mentions the specific data pulled (e.g., revenue, adverse events). It also distinguishes from siblings by noting it replaces 8–15 sequential agent calls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description provides explicit usage triggers ('when a user says compare X and Y') and examples, and differentiates between type='company' and type='drug'. It lacks explicit when-not-to-use or alternative tool names, but the guidance is clear and specific.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds that it returns tool names and descriptions, which is consistent. No contradictions, but lacks details on ranking or potential limits beyond input schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with core purpose, single paragraph, every sentence adds value. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Fully explains what the tool does, when to use it, and what it returns (names+descriptions). No output schema needed; description is complete for its simple discover function.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. Description enhances by providing example queries and noting default/max for limit, adding value beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it finds tools by describing data or task, lists broad topics, and specifies returning top-N relevant tools with names and descriptions. This distinguishes it from sibling tools like 'entity_profile' or 'compare_entities'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Call this FIRST when you have many tools available and want to see the option set (not just one answer),' providing clear when-to-use guidance and distinguishing from directly using a specific tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, making safety clear. The description adds useful behavioral context: returns specific data types, mentions citation URIs, and constraints on inputs. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that effectively front-loads purpose and usage. While about 120 words, it contains no fluff and all sentences contribute value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with no output schema, the description lists the returned data categories and mentions citation URIs. It also provides input constraints and usage guidance. Could detail response format more, but openWorldHint covers variability.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already describes both parameters. The main description repeats the schema's value parameter info but adds no new semantics beyond that.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get everything about a company' and lists specific data sources (SEC filings, fundamentals, patents, news, LEI). It distinguishes from siblings by noting it replaces calling 10+ pack tools and provides explicit usage examples.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use (e.g., user asks 'tell me about X') and when not to (if only have a name, use resolve_entity first). It also states type restrictions and input formats, providing clear guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate destructiveHint=true, so the destructive nature is known. The description adds context about clearing sensitive data, enhancing transparency beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: one sentence for purpose, one for usage guidelines, and a brief pointer to siblings. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter, no output schema, and no nested objects, the description fully covers purpose, usage, and relationship to siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with a clear parameter description for 'key.' The tool description does not add further parameter detail, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Delete a previously stored memory by key,' providing a specific verb and resource. It distinguishes itself from sibling tools like 'remember' and 'recall'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear conditions for use: 'when context is stale, the task is done, or you want to clear sensitive data.' It also advises pairing with related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description does not add any behavioral traits beyond the purpose, such as data granularity, date range, or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely short (four words) but fails to convey necessary information. It is undersized rather than appropriately concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With two parameters and no output schema, the description is grossly incomplete. It does not specify the format of the timeseries, date range, units, or any constraints.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 parameters ('mirrors', 'package') or how they affect the output. This leaves the agent without crucial information for correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Daily downloads timeseries,' which indicates the tool returns a time series of download counts, but it does not specify the package context or distinguish from sibling tools like 'recent' or 'python_major'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives such as 'recent' or 'validate_claim'. The description lacks any context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses rate limits (5 per identifier per day), cost (free, doesn't count against quota), and impact (roadmap). No contradiction with annotations (readOnlyHint false, destructiveHint false). Could mention if feedback is anonymous but still good.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, front-loaded with purpose, then usage, then constraints. No fluff, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, usage, rate limits, and impact. Missing explicit mention of return value (e.g., confirmation message), but for a feedback tool without output schema, this is acceptable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already documents each parameter (100% coverage). Description adds valuable guidance for the message parameter: describe in terms of tools/packs, don't paste prompts. This clarifies formatting beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool is for providing feedback to the Pipeworx team about bugs, missing features, or praise. It lists specific feedback types and distinguishes this from other tools on the server which are data query or analysis tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes when to use the tool (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompt). Also provides rate limit and quota information, which helps the agent decide when to invoke.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds substantial behavioral context: walks child markets, groups related markets, checks monotonicity, returns ranked opportunities with reasoning. No contradiction with annotations. Fully informs the agent of internal logic.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured with explicit 'TWO MODES' formatting. Every sentence adds value, no fluff. Front-loaded with purpose and mode distinctions. Appropriate length for the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers usage comprehensively, explains return value format ('ranked opportunities with suggested trade direction + reasoning'). However, could be slightly clearer that exactly one of 'event' or 'topic' should be provided (implied but not explicit). Otherwise complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds significant meaning beyond schema: explains the two modes, gives examples of values, and clarifies how each parameter triggers different behavior. The agent gains deeper understanding of parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it finds arbitrage opportunities on Polymarket by checking monotonicity violations. Distinguishes two specific modes (event and topic) with examples. Verb+resource is specific and differentiates from siblings like bet_research or polymarket_edges.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes when to use each mode: single-event mode for a specific event slug, cross-event mode for a topic/seed question. Explains the rationale for cross-event mode (catches cases where Polymarket lists each cutoff as its own event). Provides clear context without needing to state exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral context: it scans top markets, groups by asset, fetches price history once, computes model probability, ranks by edge, and returns top N with suggested direction. It also mentions the model (lognormal from FRED + live coinpaprika price). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph of 4 sentences, which is concise and front-loads the main purpose. It is dense but not overly long. Slightly more structured formatting could improve readability, but it's effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains the return type: 'Top N ranked by edge magnitude with suggested trade direction.' It covers input parameters implicitly through schema. It explains the model and process briefly. For a tool of moderate complexity, it is complete enough, though it lacks details on error handling or edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 3 parameters with 100% description coverage, so the schema already documents them. The description mentions defaults (limit default 10, window default 1wk, min_edge_pp default 0.5) but these are also in the schema. The description does not add new meaning beyond what the schema provides, hence baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It specifies the scope (crypto-price bets), the model used, and what it returns (top N ranked by edge magnitude with suggested trade direction). This distinguishes it from siblings like 'polymarket_arbitrage'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Built for the "what should I bet on today" question — agents/users discover opportunities without paging through hundreds of markets by hand.' This provides clear use case. However, it lacks explicit instructions on when not to use the tool or alternatives, though the context is strong enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint=true) indicate a safe read operation, but the description adds no behavioral details beyond that. There is no mention of return format, error handling, or side effects, missing an opportunity to provide value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

While the description is short (4 words), it is not informative. Conciseness should not sacrifice clarity; here it is too terse to be useful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (1 param, no output schema), the description should provide a clear understanding of the tool's function. It fails to explain what the tool does or how to use the parameter, making it incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'package' has 0% schema description coverage, and the description 'By Python major version' does not clarify what value to provide (e.g., package name, version string). The description fails to compensate for the lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'By Python major version' is extremely vague. It does not specify what action the tool performs on the package (e.g., retrieve, filter, list). The name suggests it pertains to Python major versions, but the purpose remains ambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus the sibling 'python_minor'. The description lacks any context about prerequisites, alternatives, or when to invoke this tool specifically.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds nothing beyond 'By Python minor version.' which does not disclose any behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely short but under-specified. It is not concise in a helpful way; it omits critical information that every sentence should justify.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one parameter and no output schema, the description is insufficient. It does not explain return values, filtering logic, or how the tool works, leaving the agent with inadequate context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% with no descriptions for the 'package' parameter. The description does not explain what the parameter means or how to use it, failing to compensate for the lack of schema info.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description is 'By Python minor version.' which is vague. It does not state a verb or action, and fails to clarify what the tool does. It could be a filter or search, but the purpose is unclear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like python_major. The description provides no context for usage or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, destructiveHint; description adds scoping and behavior for listing keys, no contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, front-loaded with purpose, each sentence adds value. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Simple tool with one optional param, strong annotations; description covers usage, scoping, and pairing completely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description covers parameter fully (100% coverage); description does not add new meaning beyond what schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it retrieves a value saved via remember or lists all keys. Distinguishes from sibling tools remember and forget.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use (look up context stored earlier) and pairs with remember/forget. Includes scoping details.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds little beyond the annotations. Annotations already mark it as read-only and non-destructive. The description does not disclose any behavioral traits such as rate limits, authentication needs, or what exactly 'recent period' means in terms of data recency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at one sentence, but it is under-informative. It is front-loaded but lacks essential details, making it borderline adequate.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and only two parameters, the description should be self-contained. It omits what the tool returns (e.g., a number, list) and does not clarify the period format, leaving significant gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With only 50% schema description coverage, the description should compensate but does not. It fails to explain that 'package' is required or add meaning to the 'period' parameter beyond the schema's 'day | week | month'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Total downloads in recent period' vaguely indicates the tool retrieves download counts, but it lacks a verb (e.g., 'get') and fails to mention the required 'package' parameter, making the resource unclear. It does not differentiate from the sibling 'recent_changes'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'overall' or 'recent_changes'. The agent must infer usage from context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only behavior (readOnlyHint=true, destructiveHint=false). The description adds valuable context: parallel fan-out to multiple external sources, return format (structured changes, count, URIs), and accepted date formats. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, efficient, and front-loaded with the core purpose. Every sentence adds value, covering usage examples, sources, date formats, and return structure without unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema, the description adequately explains what the tool returns (structured changes, total_changes count, citation URIs). It does not cover error cases or edge conditions, but for a monitoring tool the level of detail is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with basic descriptions. The description adds practical recommendations (e.g., use '30d' for monitoring), clarifies that 'type' only supports 'company', and provides example values for 'value' (ticker or CIK), enriching the meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool fetches recent changes (SEC filings, news, patents) for a company within a time window. It uses specific verbs ('fans out') and mentions three distinct data sources, distinguishing it from siblings like 'entity_profile' or 'overall'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit example queries and recommends default values for 'since' ('30d' or '1m'). It covers common use cases but does not explicitly state when not to use the tool or list alternative sibling tools, which would improve guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate a non-read-only, non-destructive write. The description adds critical behavioral context: key-value storage, scoping by identifier, persistence differences for authenticated vs. anonymous sessions, and a 24-hour expiration for anonymous. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (4 sentences) and front-loaded with the core action. Each sentence serves a purpose: stating the action, giving use cases, explaining storage details, and referencing sibling tools. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a write tool without an output schema, the description covers most needed context: purpose, usage, storage behavior, and scoping. It does not specify behavior on duplicate keys (overwrite vs. error), but this is a minor gap given the tool's simplicity and the presence of sibling tools for management.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with descriptions for both 'key' and 'value'. The description adds further context by providing examples of what keys might contain (e.g., 'resolved ticker') and emphasizes the key-value nature. While schema already documents parameters, the description enriches understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies the verb 'Save' and the resource 'data' with clear intent for reuse. It distinguishes from sibling tools 'recall' and 'forget' by name, and explains scoping and persistence.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use the tool: 'when you discover something worth carrying forward'. Provides concrete examples and contrasts with recall/forget for retrieval and deletion. Also notes scoping and persistence differences.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and destructiveHint=false, but the description adds valuable context: it returns multiple ID types plus citation URIs, and it consolidates multiple lookups into one call. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (4 sentences), front-loaded with the main purpose, followed by examples and usage guidance. Every sentence is informative, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the two simple parameters, high schema coverage, and no output schema, the description covers the return values (IDs + citations) and hints at efficiency gains ('Replaces 2–3 lookup calls'). It might be missing edge cases (e.g., multiple matches), but overall provides sufficient context for agentic use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage, but the description enhances understanding with concrete examples for both parameters (e.g., 'Apple' → AAPL/CIK 0000320193, 'Ozempic' → RxCUI 1991306). This clarifies how to encode values beyond the schema's brief descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Look up the canonical/official identifier') and the scope ('for a company or drug'), with specific examples of identifier types (CIK, ticker, RxCUI, LEI). It distinguishes from sibling tools by positioning itself as a prerequisite for tools that need official identifiers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use ('when a user mentions a name and you need ... the ID systems that other tools require') and gives usage order ('Use this BEFORE calling other tools that need official identifiers'). However, it does not explicitly state when not to use it, such as when identifiers are already known.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds no behavioral context beyond the annotations. It does not explain the unusual 'openWorldHint' or clarify any side effects, leaving 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

At three words, the description is under-specified, not concise. It sacrifices necessary information and does not earn its place as a useful description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema, one undocumented parameter, and vague purpose, the description is completely inadequate. An agent cannot reliably use this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the description does not mention the 'package' parameter at all. No meaning is added, leaving the agent without clue about what value to provide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'By OS/system.' is extremely vague and does not state a specific verb or resource. It fails to convey what the tool actually does, making it misleading or missing for an AI agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus its siblings (e.g., ask_pipeworx, bet_research). The description gives no context for appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) are consistent with the description, which adds useful context beyond annotations: it details the return format (verdict, structured form, actual value with citation) and efficiency gains (replaces 4-6 sequential calls). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the action and usage, and each sentence adds value. It is slightly verbose (e.g., listing synonyms for fact-check), but overall it is efficiently structured with clear sections: purpose, usage, scope, return value, and efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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 covers the essential aspects: purpose, when to use, return values, and scope. It does not mention error cases or rate limits, but given the annotations and schema coverage, the description is sufficiently complete for an AI agent to decide and invoke.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single parameter 'claim'. The description adds examples of natural-language claim formats (e.g., 'Apple's FY2024 revenue was $400 billion'), but this does not significantly enhance meaning beyond the schema's description, which already states it is a natural-language claim. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a clear verb ('fact-check, verify, validate') and specifies the resource ('natural-language factual claim') with examples. It distinguishes the tool from siblings by detailing its specific functionality and scope (company-financial claims via SEC EDGAR + XBRL), 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.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit context on when to use the tool ('when an agent needs to check whether something a user said is true') and gives example queries. It also mentions the scope limitation ('v1 supports company-financial claims') but does not explicitly state when not to use or name alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.