github

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it picks the right tool and fills arguments automatically, handles natural language input, and returns results. However, it lacks details on limitations (e.g., rate limits, error handling, or data source constraints), which prevents a perfect score.

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 functionality, followed by practical benefits and concrete examples. Every sentence adds value: the first defines the purpose, the second explains the automation, and the third provides usage examples. It's efficiently structured without 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 tool's complexity (natural language querying with automated tool selection) and lack of annotations or output schema, the description does well by explaining the process and providing examples. However, it doesn't cover potential outputs or error cases, leaving some gaps in contextual understanding for an AI 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?

Schema description coverage is 100%, so the schema already documents the single 'question' parameter as 'Your question or request in natural language.' The description adds minimal value beyond this by reiterating 'plain English' and providing examples, but doesn't explain parameter nuances like length limits or formatting. Baseline 3 is appropriate given high schema coverage.

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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask') and resource ('answer from data source'), and distinguishes itself from siblings by emphasizing natural language interaction without needing to browse tools or learn schemas. The examples further clarify the scope.

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 states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' This provides clear guidance to use ask_pipeworx for natural language queries instead of other tools that might require specific parameters or schemas, effectively differentiating it from siblings like discover_tools or search_repos.

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 safe read operations. The description adds valuable context about internal behavior: it resolves the market, classifies the bet, fans out to relevant packs, and returns an evidence packet plus comparison. This goes beyond annotations by explaining the fan-out logic (e.g., crypto+fred+gdelt for BTC), aiding the agent in understanding what happens under the hood.

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 fairly long but every sentence provides meaningful information. It is front-loaded with the core purpose and input options. While it could be slightly more concise, it avoids redundancy and is well-structured for an AI agent 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?

Despite lacking an output schema, the description fully explains the return value (evidence packet plus market-vs-model comparison). It covers inputs, outputs, use cases, internal logic, and even hints at performance (tools that get context here convert better). Given the tool's complexity and the number of sibling tools, this description is comprehensive and leaves no critical gaps.

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 documents parameters well. The description adds meaning by explaining that 'market' can be a slug, URL, or question text, and clarifies 'depth' with quick vs thorough options. This enhances the schema's completeness and helps the agent choose appropriate values.

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 specifies the tool's purpose: to research Polymarket bets by pulling Pipeworx data. It details inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). The verb 'research' combined with 'Polymarket bet' uniquely identifies its function among siblings like ask_pipeworx 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?

The description explicitly states when to use the tool: for questions like 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It positions itself as the core demo product, implying it should be preferred over general discovery tools. However, it lacks explicit 'when not to use' scenarios, though the use cases are 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?

With no annotations, the description carries full burden. It discloses the data sources (SEC EDGAR, FDA) and return format (paired data + URIs). It does not mention authentication or rate limits, but for a read-like comparison tool this is acceptable. Slightly higher score would require mention of no side effects.

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

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

The description is four sentences, front-loading the core purpose, then providing type-specific details and a concrete benefit. 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?

Despite lacking an output schema, the description fully covers the tool's behavior for both entity types, parameter constraints (2–5 items), and return information. It is complete for an AI agent to select 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%, but the description adds meaning by explaining the data returned for each type and providing examples for the 'values' parameter. This helps the agent understand parameter impact beyond schema constraints.

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 compares 2–5 entities side by side, specifies two entity types (company, drug) with distinct data returned for each, and highlights efficiency gains. This verb+resource definition distinguishes it from sibling tools which do not offer 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?

The description provides clear context on when to use the tool (comparing entities) and the benefit of reducing 8–15 sequential calls. It does not explicitly state when not to use it or mention alternatives, but sibling tools are unrelated, so the guidance is sufficient.

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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'most relevant tools' (implying ranking/ relevance scoring) and has a default/max limit (implied by the schema), but doesn't mention behavioral aspects like rate limits, authentication needs, or error handling. It adds some context but lacks comprehensive behavioral details.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second provides crucial usage guidelines. Every sentence earns its place with no wasted words, making it highly efficient 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 tool's moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description is reasonably complete. It covers purpose, usage context, and high-level behavior, but could benefit from more details on output format or error cases to be fully comprehensive.

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%, so the schema already documents both parameters ('query' and 'limit') thoroughly. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain how the query is processed or the impact of the limit). Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and distinguishes it from siblings by specifying it's for discovering tools rather than directly accessing repositories or users. It explicitly mentions returning 'most relevant tools with names and descriptions'.

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 usage guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context about when to use it (large tool catalog, discovery phase) and implies alternatives (direct tool calls) when the catalog is smaller or tools 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?

With no annotations, the description fully discloses behavior: it bundles multiple data sources, returns citation URIs, and replaces 10-15 agent calls. It also notes performance implications for federal contracts, adding valuable context beyond the 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?

Four efficient sentences, front-loaded with purpose, then details, then alternatives. No redundant 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?

Without an output schema, the description explains what is returned (SEC filings, financials, patents, news, LEI, URI format). It covers parameter details, usage guidance, and performance context, making it complete for an entity 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 coverage is 100%, and the description adds context beyond the schema: it reiterates the type limitation, explains value can be ticker or CIK, and explicitly warns that names are not supported, directing users to resolve_entity.

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: 'Full profile of an entity across every relevant Pipeworx pack in one call.' It lists specific data returned for company type and distinguishes from siblings by mentioning alternative tools for federal contracts and name resolution.

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 (for full company profile) and when-not-to-use (for federal contracts, use usa_recipient_profile directly). Also advises using resolve_entity if only a name is available, offering clear guidance on alternatives.

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?

No annotations are provided, so the description carries full burden. While 'Delete' implies a destructive mutation, it doesn't disclose whether this operation is reversible, what permissions are required, whether there are confirmation prompts, or what happens on success/failure. For a destructive tool with zero annotation coverage, this is insufficient.

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

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

The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple tool and front-loads the essential 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?

For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects like irreversibility, error conditions, or response format, leaving significant gaps for an AI agent to understand how to use this tool safely and effectively.

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% (the 'key' parameter is fully documented in the schema), so the baseline is 3. The description adds no additional parameter information beyond what's already in the schema, maintaining this baseline score.

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 ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'recall' or 'remember' that might also interact with stored memories, preventing a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'recall' (likely to retrieve memories) and 'remember' (likely to store memories), there's no indication of when deletion is appropriate versus retrieval or storage operations.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates this is a read operation ('Get') and lists example return fields, but does not cover aspects like rate limits, authentication needs, error handling, or pagination. The description adds basic context but lacks depth for a tool with no annotation support.

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

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

The description is efficiently structured in two sentences: the first states the action and parameters, and the second lists example return data. Every sentence adds value without redundancy, and it is front-loaded with the core purpose. No wasted words or unnecessary details.

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 annotations and no output schema, the description provides basic purpose and parameter context but lacks completeness. It does not explain the full return structure, error cases, or behavioral traits like rate limits. For a read tool with 2 parameters, this is adequate but has clear gaps in operational guidance.

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 clear documentation of both required parameters (owner and repo) including examples. The description adds minimal value beyond the schema by mentioning these parameters in context ('by owner and repo name'), but does not provide additional syntax, format details, or constraints. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Get full details'), resource ('GitHub repository'), and scope ('by owner and repo name'), distinguishing it from siblings like get_user (user-focused) and list_repo_issues/issues-focused). It provides concrete examples of returned data like stars and language, making the purpose explicit and differentiated.

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 implies usage for retrieving detailed repository information when owner and repo name are known, but does not explicitly state when to use this tool versus alternatives like search_repos (for broader searches) or get_user (for user data). No exclusions or prerequisites are mentioned, leaving some ambiguity in tool 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool returns public profile data, implying a read-only operation, but doesn't specify authentication needs, rate limits, error conditions, or whether it's safe for repeated use. This leaves significant gaps in understanding the tool's behavior beyond basic functionality.

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 and front-loaded, starting with the core purpose in the first sentence. The second sentence efficiently lists key return fields without unnecessary elaboration. There's minimal waste, though it could be slightly more structured by explicitly separating purpose from output details.

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 tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and output fields but lacks behavioral context like authentication or error handling. Without annotations or an output schema, more detail on usage and limitations would improve completeness for agent decision-making.

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% description coverage, with the 'username' parameter clearly documented. The description doesn't add any parameter-specific details beyond what the schema provides, such as format constraints or examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't enhance parameter 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 clearly states the tool's purpose: 'Get the public profile of a GitHub user.' It specifies the verb ('Get') and resource ('public profile of a GitHub user'), making the action and target explicit. However, it doesn't distinguish this tool from potential siblings like 'get_repo' beyond the resource type, which keeps it from a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions what data is returned but offers no context on prerequisites, limitations, or comparisons to sibling tools like 'get_repo' or 'search_repos'. This lack of usage context leaves the agent without clear direction for tool 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return fields (title, number, state, labels, created_at) but fails to cover critical aspects like pagination behavior (implied by 'per_page' parameter), rate limits, authentication needs, or error handling, leaving significant gaps for a tool that interacts with an external API like GitHub.

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, efficient sentence that front-loads the core purpose and key return fields, with no wasted words. It is appropriately sized for the tool's complexity, making it easy to parse quickly.

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 tool's complexity (interacting with GitHub API, 4 parameters, no output schema), the description is incomplete. It lacks details on output structure beyond listed fields, pagination, error cases, or API constraints, which are crucial for effective use without annotations or output schema.

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% description coverage, documenting all parameters clearly. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or usage examples, so it meets the baseline for high schema coverage without compensating value.

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 ('List') and resource ('issues for a GitHub repository'), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'get_repo' or 'search_repos', which might also involve repository data, so it misses full sibling differentiation.

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

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 such as 'search_repos' or 'get_repo', nor does it mention any prerequisites or exclusions. It lacks explicit context for tool selection, leaving usage implied at best.

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?

No annotations are provided, so the description carries the full burden. It discloses rate limiting and implies non-destructive behavior (sending feedback), but does not explain what happens after submission (e.g., confirmation, async processing). Adequate but not comprehensive.

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 three sentences, front-loaded with purpose, followed by use cases and constraints. Every sentence serves a purpose, 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 simple feedback tool with no output schema, the description covers purpose, usage guidelines, content rules, and rate limits. It is sufficient for an agent to use correctly, though more detail on response behavior could be added.

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%, so the baseline is 3. The description adds value by advising on message content ('describe what you tried...'), which is not in the schema. This extra guidance improves usability.

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: 'Send feedback to the Pipeworx team.' It lists specific use cases (bug reports, feature requests, missing data, praise), distinguishing it from sibling tools that query or manipulate data.

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 guidance (bug reports, feature requests, etc.) and content rules (describe what you tried, do not include user prompt verbatim). It also mentions rate limits (5 per day), but does not specify when not to use it or suggest alternatives.

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, and the description adds valuable behavioral context: it 'walks child markets', 'searches across separate events', 'groups them, then checks monotonicity', and 'returns ranked opportunities with suggested trade direction + reasoning.' There is no contradiction with annotations, and the description fully discloses the tool's behavior.

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

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

The description is concise (approximately 6 sentences) and well-structured: it opens with the main purpose, then details the two modes in a clear, bullet-like manner, and ends with the return value. 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?

Given the 100% schema coverage, presence of annotations, and no output schema, the description adequately covers what the tool does and returns. It mentions return format ('ranked opportunities with suggested trade direction + reasoning'). However, it could be improved by noting any prerequisites (e.g., internet access) or potential edge cases (e.g., no opportunities found), but overall it is sufficiently 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 description coverage is 100%, setting baseline at 3. The description adds significant extra meaning by explaining the two modes (event vs. topic) and providing concrete examples (e.g., 'when-will-bitcoin-hit-150k' and 'Strait of Hormuz traffic returns to normal'), which clarifies usage beyond the schema's generic 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?

Description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It specifies two modes (event and topic) and distinguishes from sibling tools like polymarket_edges by explaining when to use each. The verb 'find' and resource 'arbitrage opportunities' are specific and action-oriented.

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 details two modes and provides context on when to use each: 'single-event mode misses the May≤June rule' for cross-event mode. It gives an example of cross-event mode catching cases where Polymarket lists each cutoff as its own event. While it doesn't explicitly state when not to use, the clear mode selection criteria provide sufficient 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 read-only, non-destructive operation. Description adds context: scans top markets, groups by asset, fetches price history once, computes model probabilities, ranks by edge. No contradictions 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 relatively concise but includes extraneous details about the model (V1, lognormal from FRED + coinpaprika) that could be shortened or moved elsewhere. Front-loaded with main action, but slightly 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 no output schema, description adequately hints at return structure (top N ranked by edge magnitude with suggested trade direction). Explains the process and data sources. However, missing details on limitations or prerequisites for the model.

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 100% of parameters with descriptions (limit, window, min_edge_pp). Description does not add new details beyond schema, so baseline 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?

Description clearly states it scans high-volume Polymarket markets to find where Pipeworx data disagrees with market prices, using a specific model (lognormal from FRED + coinpaprika). It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on edge discovery for betting opportunities.

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 the tool is built for 'what should I bet on today' and automates paging through markets. However, it does not specify when not to use or mention alternatives like polymarket_arbitrage for arbitrage opportunities, leaving some room for ambiguity.

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?

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool can retrieve individual memories or list all, works across sessions, and accesses previously stored data. However, it doesn't mention error handling, performance characteristics, or data format details.

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 perfectly concise with two sentences that each serve distinct purposes: the first explains the dual functionality, the second provides usage context. Every word earns its place with zero 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?

Given the tool's moderate complexity (single optional parameter, no output schema, no annotations), the description is nearly complete. It explains what the tool does, when to use it, and parameter behavior. The main gap is lack of information about return format or error 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?

The schema description coverage is 100%, so the baseline is 3. The description adds meaningful context by explaining that omitting the key parameter triggers listing all memories, which clarifies the optional parameter's semantic effect beyond what the 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?

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by mentioning 'context you saved earlier' which relates to the 'remember' sibling tool, establishing a clear relationship.

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 ('retrieve context you saved earlier in the session or in previous sessions') and when to omit parameters ('omit key to list all keys'). It distinguishes from alternatives by referencing 'remember' as the complementary save operation.

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?

No annotations provided, so description carries full burden. Describes parallel fan-out to multiple sources, return structure (structured changes, total_changes count, pipeworx:// URIs). Missing explicit read-only hint, but the behavior is well-explained.

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 sentences, front-loaded with purpose. Each sentence contributes unique information: purpose, fan-out mechanics, return format, use cases. No redundant or irrelevant 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?

Self-contained description covering functionality, parameter usage, and return structure. With no output schema or annotations, it adequately explains the tool. Could mention pagination or rate limits, but not critical 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%, baseline at 3. Description adds value: explains 'since' accepts ISO dates and relative strings like '30d', and 'value' takes ticker or zero-padded CIK. Examples clarify 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?

Clearly states it shows 'what's new about an entity since a given point in time' and specifies the entity type (company) and data sources (SEC EDGAR, GDELT, USPTO). Distinguishes from siblings like entity_profile by focusing on change monitoring.

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 gives use cases: 'brief me on what happened with X' or change-monitoring workflows. Provides accepted formats for 'since' parameter. Does not mention when NOT to use, but the context is clear. Could reference siblings like entity_profile for static profile needs.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it's a write operation (implied by 'store'), has authentication-dependent persistence (authenticated vs. anonymous), and specifies session scope. However, it doesn't mention potential limitations like storage limits or error conditions.

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

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

The description is perfectly concise and front-loaded: the first sentence states the core purpose, the second provides usage context, and the third adds important behavioral detail. Every sentence earns its place with zero 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 2-parameter write tool with no annotations and no output schema, the description does well by explaining purpose, usage context, and persistence behavior. It could be more complete by mentioning what happens on duplicate keys or storage limits, but covers the essential context given the tool's complexity.

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%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain key constraints or value formatting). Baseline 3 is appropriate when the schema does all the work.

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 with specific verb ('store') and resource ('key-value pair in your session memory'), and distinguishes it from sibling tools like 'recall' (which likely retrieves) and 'forget' (which likely deletes). It explicitly mentions what gets stored and where.

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 this tool ('save intermediate findings, user preferences, or context across tool calls') and includes important context about authentication differences (persistent vs. 24-hour memory), which helps distinguish it from alternatives like 'recall' for retrieval.

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?

No annotations are provided, so the description carries full burden. It discloses accepted input formats and return values, but does not explicitly state whether the operation is read-only or mention rate limits or authentication needs. The behavior is implied but not fully transparent.

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, with two sentences. The first sentence states the core purpose, and the second provides essential details about inputs and outputs. 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 low complexity (two parameters, no nested objects, no output schema), the description fully covers what the tool does, what it accepts, and what it returns. It explains the return values in detail.

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 providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and clarifying the acceptable formats for the 'value' parameter, going 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 resolves an entity to canonical IDs, specifying the entity type (company) and the output (ticker, CIK, name, URIs). It differentiates itself by noting it replaces multiple lookup calls, making its purpose distinct.

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 indicates when to use the tool ('in a single call', 'replaces 2-3 lookup calls'), implying efficiency benefits. It provides concrete input examples but does not explicitly state when not to use it or mention alternative sibling 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return format (name, full_name, etc.) and scope ('top results'), which adds value beyond the schema. However, it lacks details on rate limits, authentication needs, pagination, or error handling, which are important for a search 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?

The description is a single, efficient sentence that front-loads the core action and resource, followed by key return details. Every word earns its place with no redundancy or unnecessary elaboration, making it highly concise and well-structured.

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

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

Given the tool's moderate complexity (search with 3 parameters) and no annotations or output schema, the description is reasonably complete. It covers the purpose, resource, and return fields, but lacks behavioral details like rate limits or error handling. It's adequate for basic use but could be more comprehensive.

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%, so the schema fully documents all three parameters (query, sort, per_page). The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints not covered. Baseline 3 is appropriate as the schema handles 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?

The description clearly states the specific action ('Search GitHub repositories by keyword') and resource ('GitHub repositories'), distinguishing it from sibling tools like get_repo (fetch single repo), get_user (user info), and list_repo_issues (issue listing). It specifies the scope ('top results') and output fields, 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?

The description implies usage for keyword-based repository searches but doesn't explicitly state when to use this tool versus alternatives like get_repo (for specific repos) or list_repo_issues (for issues). No exclusions or prerequisites are mentioned, leaving some ambiguity about optimal use cases.

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?

No annotations are provided, so the description must disclose behavioral traits. It describes the output (verdict, values, citation) and scope (v1 supports company-financial claims), but does not mention side effects, authorization needs, or error handling. The behavior is minimally transparent.

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 concise: two sentences that front-load the key purpose and then provide additional context. 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?

Given the simple input schema and no output schema, the description provides a solid overview. It specifies supported domains (company-financial claims, US public companies), the data sources, and the output structure. It could be more complete by listing all supported financial metrics explicitly, but it is largely 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?

The input schema already has 100% description coverage for the single 'claim' parameter, with a clear example. The tool description adds value by specifying the supported claim type (company-financial) and output format, but does not add new meaning about the parameter itself 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's purpose: fact-checking natural-language claims against authoritative sources, specifically company-financial claims for public US companies via SEC EDGAR and XBRL. It is distinct from siblings like compare_entities and resolve_entity, which focus on entity comparisons and resolution.

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 implies the tool is for fact-checking financial claims and mentions it replaces multiple sequential agent calls, giving a clear use case. However, it lacks explicit guidance on when not to use it (e.g., for non-financial claims) and does not suggest alternatives.

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