Mast Nasa

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

Annotations already mark as readOnly (safe). Description adds the scale (1,423+ tools, 392+ sources) and output format (structured answer with pipeworx:// citation URIs), providing behavioral context 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?

Multiple sentences but each adds value: preference statement, mechanism, usage cues, examples. Front-loaded with key guidance. Slightly verbose but efficient for the information conveyed.

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 simple input schema and no output schema, the description fully covers purpose, usage, input examples, and output format. No obvious gaps for this router-style 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 has 100% coverage for single parameter 'question' with minimal description. The tool description greatly enriches meaning by specifying the types of questions, domains, and examples, making the parameter semantics clear.

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 the tool is a preferred alternative to web search for answering questions from authoritative structured data sources. Lists specific domains and explains it dispatches to over 1,400 tools, distinguishing it from siblings like 'bet_research' and 'mission_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?

Explicitly says 'PREFER OVER WEB SEARCH' and provides usage triggers like 'what is', 'look up', etc. Gives examples. Does not mention cases to avoid, but the positive guidance 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?

Beyond annotations (readOnly, openWorld, non-destructive), the description details the tool's behavior: it resolves the market, classifies the bet (crypto, Fed, etc.), fans out to relevant packs (e.g., crypto+fred+gdelt), and returns evidence with comparison. 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 front-loaded with the core purpose, then explains functionality and use cases. All sentences are relevant, though slightly lengthy. It could be trimmed slightly but remains well-structured and informative.

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 complexity (multiple data packs, classification, comparison) and no output schema, the description sufficiently explains the return value (evidence packet + market-vs-model comparison) and the fan-out logic. The tool's purpose is fully covered.

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 baseline is 3. The description adds value by explaining the 'market' parameter accepts slug, URL, or question text, and clarifies that 'depth' defaults to 'thorough'. This goes beyond the schema 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 tool researches a Polymarket bet by pulling Pipeworx data. It specifies the input types (slug, URL, question text) and the outputs (evidence packet, market-vs-model comparison), distinguishing it from sibling tools like validate_claim 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?

The description explicitly recommends use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It also notes this is the core demo product. However, it does not explicitly state when not to use it or mention alternatives, though the context is clear.

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 no behavioral context beyond 'query', but does not contradict annotations. With annotations covering the basics, a baseline score of 3 is appropriate.

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 sentence, which is concise but does not sacrifice unnecessary words. However, it is too terse to be informative, earning a middle score.

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 complexity of a nested 'query' object and no output schema, the description is insufficient. It does not explain the format of the request body, what the CAOM query returns, or provide any usage hints. Agents are left guessing.

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%, with parameter 'query' described as 'Raw MAST request body (service + params).' The tool description does not elaborate on this parameter, so it adds no value beyond the schema. Baseline of 3 applies.

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 'Generic CAOM (Common Archive Observation Model) query' is vague and tautological. It doesn't specify what data is accessed or what the query returns, and does not distinguish itself from sibling tools like 'cone_search' which likely performs similar astronomical searches.

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. There is no mention of prerequisites, context, or exclusion criteria, leaving the agent without direction for appropriate invocation.

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 mark as read-only and non-destructive. Description adds data sources (SEC EDGAR/XBRL, FAERS), specific fields (revenue, net income, etc., adverse events, approvals), and return format (paired data + URIs). 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?

Description is well-structured with core purpose first, then usage triggers, then type details. Slightly verbose but every sentence serves a purpose (examples, data sources, benefits). Efficient 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?

Given no output schema, description fully explains return content (paired data, citation URIs). Covers both entity types, data sources, and use cases. Complete for an agent to decide and invoke correctly.

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%. Description adds meaning: explains type enum values ('company' vs 'drug') and what data each fetches, and provides examples for values array (tickers for company, drug names). Adds 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?

Description clearly states the tool compares 2-5 companies or drugs side by side, with specific verbs ('compare', 'side by side') and resource types ('companies', 'drugs'). It distinguishes from siblings like entity_profile by focusing on multi-entity comparison.

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 (user says 'compare X and Y', 'X vs Y', 'stack up', etc.) and lists example scenarios. Implicitly excludes single-entity queries which sibling entity_profile handles.

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 safe read nature is clear. The description adds no behavioral context beyond that, which is acceptable but not adding 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?

The description is very concise at one sentence. It is front-loaded with the action. However, it may be too sparse, omitting useful details without becoming verbose.

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 complexity of 5 parameters and no output schema, the description is incomplete. It fails to explain what MAST is, what results look like, or how it differs from similar tools like 'mission_search'.

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 only 40%, with ra, dec, and limit lacking descriptions. The tool description does not mention any parameters or compensate for the missing schema details.

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 the tool searches MAST around (RA, Dec), which is a clear verb+resource. However, it could be more specific about what data is returned, and it doesn't differentiate from sibling tools like 'mission_search' or 'caom'.

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 no guidance on when to use this tool versus alternatives. There is no mention of when not to use it or any prerequisites.

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 top-N relevant tools with names and descriptions, providing sufficient behavioral context for 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.

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

Well-structured and concise: starts with purpose, then usage guidance, then examples. 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?

No output schema, but description explains it returns top-N relevant tools with names and descriptions, which is adequate for a discovery tool. No missing critical details.

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%. Description adds examples for the query parameter and clarifies its purpose beyond the schema, though it doesn't repeat the limit parameter description.

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 many domains, and distinguishes itself from sibling tools like entity_profile or cone_search by being a discovery tool.

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 to use when needing to browse/search/discover tools, and to call it first when many tools are available. Could be more specific on when not to use, but provides practical 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 declare readOnlyHint=true and destructiveHint=false. The description adds behavioral details: returns multiple data types (SEC filings, financials, patents, news, LEI) with pipeworx:// citations. It also notes the current limitation: only 'company' type supported.

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 starts with the main purpose, lists use cases, and enumerates return data. Every sentence adds value, no wordiness.

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 but description lists return fields (SEC filings, revenue, patents, etc.). Covers input requirements and data sources. Lacks details on pagination, limits, or ordering, but adequate for a profile 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 has 100% description coverage. The description adds valuable context beyond schema: explains value can be ticker or zero-padded CIK, and explicitly says names are not supported, directing to resolve_entity. It also clarifies the enum for type.

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 'Get everything about a company in one call' and provides concrete use-case phrases like 'tell me about X' and 'research Microsoft'. It clearly identifies the verb (get) and resource (company profile), distinguishing it from sibling 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?

The description advises when to use this tool (for company profile requests) and what to do if only a name is available ('use resolve_entity first'). This provides explicit usage guidance and an alternative.

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 description does not need to reiterate destructiveness. It adds context about clearing stale or sensitive data, which goes beyond the annotation and helps the agent understand appropriate use.

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?

Two concise sentences: first states purpose, second gives usage guidelines. Efficiently front-loaded with 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?

For a simple tool with one parameter and no output schema, the description covers purpose, usage guidance, and sibling linkage completely. No missing critical information.

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% with one parameter 'key' described as 'Memory key to delete'. The description does not add additional meaning beyond that, so scores baseline 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 explicitly states 'Delete a previously stored memory by key', using a specific verb (delete) and resource (memory by key). It distinguishes from siblings by mentioning pairing with remember and recall, providing clear differentiation.

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?

Provides explicit when-to-use conditions: 'when context is stale, the task is done, or you want to clear sensitive data'. Mentions pairing with siblings but lacks explicit when-not-to-use guidance. Still, the conditions are clear and actionable.

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 no additional behavioral context (e.g., error handling, return format caveats).

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?

Extremely concise (5 words), but sacrifices clarity. Could benefit from a brief sentence about expected input format or output type without becoming verbose.

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 parameter, no output schema), the description is too sparse to fully understand its role among 16 sibling tools. Missing information about coordinate system or name resolution logic.

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%, so the description carries full burden. It only says 'name' is the input but does not specify format, allowed values, or how resolution works (e.g., is it a database lookup? API call? Ambiguous).

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 clearly states verb 'Resolve' and resource 'target name → coords', but lacks differentiation from sibling tools like 'resolve_entity'. The exact meaning of 'target name' and 'coords' is 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 alternatives like 'resolve_entity' or 'entity_profile'. No prerequisites or context about when coordinate resolution is needed.

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 readOnly and openWorld hints. The description adds 'arbitrary criteria' implying flexible querying, but no additional behavioral context like performance or pagination.

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?

A single concise sentence with no redundancy, but may be too brief relative to the tool's 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?

No output schema and no description of return values. The tool deals with nested objects and search results, but the description omits what the user can expect.

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 67% (2 of 3 params described). The description adds 'arbitrary criteria' which is vague and does not compensate for the undocumented 'limit' parameter.

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's a mission-scoped search with arbitrary criteria, which is specific enough. However, it does not differentiate from sibling tools like cone_search 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?

No guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The description is too brief to inform 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), free usage, and that it doesn't count against tool-call quota. No contradiction with annotations (readOnlyHint false is appropriate for a writing tool).

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 yet informative: opens with purpose, then usage criteria, style guidance, and behavioral notes. Every sentence adds value with no redundancy.

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 feedback tool with no output schema, the description covers all necessary aspects: purpose, when to use, how to write messages, rate limits, and impact on roadmap. Fully adequate.

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 value by explaining the enum values for 'type' and reinforcing that 'message' should be specific without pasting user prompts. This goes beyond a baseline 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 the tool's purpose: to tell the Pipeworx team about bugs, missing features, or praise. It distinguishes from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on feedback.

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 specifies when to use (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompt). Also mentions rate limits and quota exemption, providing clear usage boundaries.

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, openWorldHint=true, destructiveHint=false. The description adds that the tool returns ranked opportunities with suggested trade direction and reasoning, and explains the searching and grouping behavior in cross-event mode. 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 slightly lengthy but every sentence adds value, with the main purpose front-loaded. The two modes are clearly separated. A minor trim could improve conciseness, but it 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?

Given the complexity (two modes, cross-event logic) and no output schema, the description is quite complete: it explains functionality, mode selection, and return value (ranked opportunities with reasoning). It lacks information on possible errors or data currency, but it is sufficient for most use 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?

Schema coverage is 100% with descriptions for both parameters. The description expands significantly: for 'event' it clarifies walking child markets, for 'topic' it explains searching across separate events and grouping. This adds meaningful context 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 it finds arbitrage opportunities on Polymarket by checking monotonicity violations. It distinguishes two specific modes (event and topic) and uses a specific verb-resource pair ('Find arbitrage opportunities'). This differentiates it from siblings like '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?

The description explicitly explains when to use each mode: event for a single event's child markets, topic for cross-event searches. It includes an example of cross-event catch. However, it does not explicitly mention when not to use the tool or mention alternatives beyond the two modes.

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=true and destructiveHint=false, indicating safety. Description adds detailed internal behavior: scans top markets, groups by asset, fetches price history once, computes model probability, ranks by edge. It also mentions data sources (FRED, coinpaprika). 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?

Description is a tight, single paragraph of 4-5 sentences. It front-loads the main purpose, then provides details on method, data sources, and return value. No redundant or extraneous sentences.

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?

Despite 3 parameters, no output schema, and no nested objects, the description fully explains the tool's purpose, internal logic, data sources, and what it returns (top N ranked by edge with suggested trade direction). It is complete for an agent to decide when and how to use it.

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% description coverage for all three parameters (limit, window, min_edge_pp). Description does not add extra meaning beyond schema defaults. According to guidelines, high coverage (>80%) yields baseline of 3, as the schema already provides sufficient 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?

Description clearly states the tool scans Polymarket markets and returns those with highest disagreement between Pipeworx data and market price. It specifies the action (scan, return), resource (Polymarket markets), and criteria (edge magnitude). It is tailored for opportunity discovery and distinguishes itself from siblings like polymarket_arbitrage by focusing on edge analysis.

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 explicitly states it is built for the 'what should I bet on today' question, providing clear context for when to use. It explains the tool's coverage (crypto-price bets, models from FRED and coinpaprika). However, it does not explicitly mention when not to use or compare with alternatives among siblings, like polymarket_arbitrage.

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, so description's reinforcement is consistent but not additive. It adds scoping details but doesn't contradict annotations. Score reflects that annotations carry the primary transparency burden.

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?

Three concise sentences, each serving a distinct purpose: core functionality, usage examples, and scoping/lifecycle. 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 optional parameter and no output schema, the description is complete. It covers retrieval and listing modes, scope, and sibling tools. Minor gap: no mention of error cases or data persistence behavior, but acceptable given simplicity.

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 a clear description of the 'key' parameter. Description adds value by explaining behavior when key is omitted (list all keys), which goes beyond bare 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?

Description clearly states the tool retrieves saved values via 'remember' or lists all keys when key is omitted. It distinguishes from siblings 'remember' and 'forget' by explicitly pairing with them, making the purpose unambiguous.

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

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

Provides explicit guidance: use to look up previously stored context (ticker, address, notes) without re-deriving. Mentions scope by identifier and pairs with remember/forget for full lifecycle management.

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 significant behavioral context beyond annotations, such as the parallel fan-out to SEC EDGAR, GDELT, and USPTO, and the return structure including citation URIs. 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 at just a few sentences, front-loaded with purpose, and every sentence adds value. No unnecessary information.

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 format (structured changes, count, citation URIs). It covers all parameters and sources. While it doesn't mention pagination or limits, it is complete for typical 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?

Schema coverage is 100%, providing baseline parameter descriptions. The description adds valuable context, e.g., for 'since' it explains ISO date and relative shorthand formats, and for 'value' it gives examples of ticker and CIK. This exceeds the 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 the tool's purpose: answering 'what's new' about a company using multiple data sources. It includes example queries and distinguishes itself from other tools by its specific function.

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 the tool with example queries like 'what's happening with X?' and 'any updates on Y?'. It does not explicitly state when not to use it or list alternatives, but the context is clear.

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?

Beyond annotations (readOnlyHint=false, destructiveHint=false), description adds behavioral details: memory is key-value, scoped by identifier, persistence differs by auth status, and retention policy for anonymous sessions.

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 three sentences, front-loaded with purpose, then usage, then behavioral details. No wasted words, 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?

Given no output schema, description fully explains storage behavior, persistence, and pairing with other tools. Complete for a simple key-value store with one action.

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 high coverage (100%) with descriptions for key and value. Description adds context about key-value pair nature and scoping, but schema already defines parameters well.

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 clearly states the tool saves data for later reuse, with concrete examples like resolved ticker, target address, user preference. It distinguishes itself from sibling tools recall 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: 'when you discover something worth carrying forward.' Provides context that memory is scoped by identifier and that authenticated users get persistent memory while anonymous sessions have 24-hour retention. Mentions pairing with recall and forget.

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, openWorldHint, destructiveHint) are present. Description adds return details (IDs plus pipeworx:// citation URIs) and examples, complementing annotations without contradiction.

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?

Five concise sentences, front-loaded with core purpose. Every sentence adds value: purpose, usage context, examples, return info, and efficiency claim. No redundant text.

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?

Despite having no output schema, the description covers return values (IDs, citation URIs) and usage context. With only 2 simple params and rich examples, it is a complete definition for the tool's purpose.

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 params with descriptions (100% coverage). Description enriches by clarifying value parameter input formats (ticker, CIK, name for companies; brand/generic for drugs) and enum usage.

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 resource ('company or drug') with specific ID systems (CIK, ticker, RxCUI, LEI). Examples differentiate it from sibling tools like lookup_name.

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 ('when a user mentions a name and you need...') and ordering ('Use this BEFORE calling other tools'). Implies when not needed (if ID already known). No explicit mention of alternatives, but the context is strong.

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 openWorldHint=true. Description adds context: returns verdict with structured form, actual value with citation, and percent delta. Discloses v1 scope (company-financial claims). 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?

Five sentences, each adding value. Front-loaded with purpose, followed by usage, scope, and output details. 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?

Given the simple schema (1 param, no output schema) and annotations, the description provides complete context: input format, data source, output structure, and intended use case.

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 1 parameter with 100% coverage. Description adds examples and clarifies expected format (natural-language claim). Baseline 3 is appropriate as schema already does the heavy lifting.

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 clearly states the tool fact-checks natural-language claims against authoritative sources, with specific domain (company-financial) and data source (SEC EDGAR + XBRL). It distinguishes from sibling tools like compare_entities or lookup_name by its unique function.

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 'Use when an agent needs to check whether something a user said is true' and provides example queries. Does not mention when not to use, but the scope (company-financial claims) is implied.

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