L2beat

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

Annotations are absent, so description must carry transparency. It does not mention data freshness, pagination, rate limits, or what happens when no data is available—critical gaps for a data-fetching 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?

One concise sentence that front-loads the core functionality. No wasted words, though it could briefly mention default behavior for parameters.

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 2-param tool with no output schema, the description is mostly adequate but omits default parameter values and return format details, leaving some 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% with descriptions for both parameters. The tool description adds no extra meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states it provides daily transaction counts and optionally UOPS. No sibling tool offers the same data, making purpose distinct and specific.

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 explicit guidance on when to use this tool versus alternatives like entity_profile or recent_changes. Usage is implied but not contrasted with siblings.

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. Description explains it picks the right tool and returns results, but doesn't detail behavioral aspects like rate limits, authentication, response format, or handling of ambiguous questions. 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?

Description is concisely structured: purpose first, then usage guidance, then examples. Every sentence is informative 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 tool with one simple parameter, no output schema, and no annotations, the description provides sufficient context: what it does, when to use, example queries, and scope of sources.

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 100% with one parameter 'question'. Description adds value by specifying example questions and clarifying the tool is for natural-language queries, going beyond the schema's 'Your question or request in natural language'.

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 answers natural-language questions by automatically selecting the right data source, listing many sources and examples. It distinguishes itself from sibling tools like entity_profile or compare_entities by positioning as a general question-answering router.

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 a user asks... and you don't want to figure out which Pipeworx pack/tool to call' and provides question examples. Lacks explicit when-not-to-use but 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 provide readOnlyHint=true and openWorldHint=true. The description adds detailed behavioral context: market resolution, bet classification, fan-out to packs, and return of evidence packet plus market-vs-model comparison. It also explains the depth parameter's behavior. 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 five sentences long and well-structured. It front-loads the purpose, then covers inputs, process, use cases, and a business justification. Every sentence adds value, though it could be slightly more concise.

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

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

Despite no output schema, the description explains the return type: 'evidence packet plus a simple market-vs-model comparison'. It covers all parameters, use cases, and the overall process. Given the tool's complexity (multi-step research), this is sufficiently complete for an agent to understand 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% with descriptions for both parameters. The description adds value by explaining that 'market' accepts slug, URL, or question text, and by clarifying the 'depth' parameter's meaning: 'quick = 2-3 evidence sources, thorough = full fan-out'. This enhances understanding 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 verb 'Research', the resource 'Polymarket bet', and the action 'pulling relevant Pipeworx data'. It gives specific input examples and distinguishes from sibling tools like validate_claim by focusing on bet research with data integration.

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 lists use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It positions itself as the core tool for bet research but does not explicitly state when not to use it or mention alternatives among siblings.

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?

Disclosures data sources (SEC EDGAR/XBRL, FAERS, FDA) and return types (paired data + citation URIs). No annotations provided, so description carries full burden. Limitations like data freshness or US-only not mentioned.

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 with purpose first, then use cases, then details. Could trim a few redundant phrases but overall efficient.

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

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

No output schema but describes return value (paired data + citation URIs). Completeness is adequate for a comparison tool, though return structure could be more detailed.

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% but description adds value with examples for 'type' and 'values', explaining formats ('tickers/CIKs' for company, 'drug names' for drug) and constraints (2-5 items).

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

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

Clearly states it compares 2-5 companies or drugs side by side, with specific data sources and use cases. Distinguishes from siblings like entity_profile (single entity) and validate_claim.

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 lists triggering phrases ('compare X and Y', 'X vs Y', 'stack up') and states it replaces many sequential calls. Could mention when NOT to use (e.g., single entity needs entity_profile).

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 must carry the full burden. It mentions the output format (top-N most relevant tools with names and descriptions) and suggests a read-only nature, but does not disclose potential side effects or authentication needs. 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 concise and front-loaded with the core purpose. The list of domains, while long, is informative. Could be slightly more concise by omitting full domain list, but overall 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 tool's simplicity, the description is complete: it explains the purpose, usage scenario, and return format. It does not have an output schema but describes what to expect. Lacks only a note about scope (e.g., only this server's tools), but that is implied.

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 explains the 'query' parameter as natural language and the 'limit' as maximum number, but the schema already provides detailed descriptions. No additional semantic value beyond schema.

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

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

The description clearly states 'Find tools by describing the data or task', specifies the tool's function as browsing/searching/discovering, and lists numerous domains. It explicitly distinguishes itself from siblings by advising 'Call this FIRST when you have many tools available'.

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 on when to use ('when you need to browse, search, look up, or discover') and suggests calling first for exploring options. It does not explicitly state when not to use, but the context strongly implies it.

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 full burden. It transparently discloses the returned data types (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. However, it lacks notes on data freshness or limits.

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 but efficiently front-loads the purpose. Every sentence adds information, though a bit dense. Could benefit from slight restructuring but remains concise.

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

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

No output schema exists, so description fully explains return values (SEC filings, revenue, etc.). It covers the multi-source nature of the tool and provides adequate context for an agent to understand the output.

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 3. The description adds value by explaining that only 'company' type is supported and that value must be ticker or CIK (names require resolve_entity). This goes 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: 'Get everything about a company in one call.' It specifies use cases (e.g., 'tell me about X') and lists the data returned (SEC filings, revenues, patents, news, LEI), distinguishing it from calling multiple 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 provides explicit guidance on when to use (user asks for company info) and includes a negative usage: if only a name is available, use resolve_entity first. This effectively helps the agent decide when to invoke this tool vs. 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 provided; description indicates destructive action but does not elaborate on side effects (e.g., irreversibility, missing key handling). Adequate but not rich.

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 sentences, front-loaded with purpose, then usage guidance. No wasted words, concise and clear.

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, description covers purpose, usage conditions, and sibling relationships. Complete enough for effective 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% and schema already describes parameter as 'Memory key to delete'. Description adds no further semantic value beyond what is in 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 action ('Delete a previously stored memory by key') and resource ('memory'), and distinguishes from siblings by referencing 'pair with remember and recall'.

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

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

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data', providing clear context and alternatives (sibling tools named).

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, but the description discloses the output includes risks, stage, milestones, and contracts, implying a read operation. However, it does not specify behavior on missing slugs (likely error), authentication requirements, or rate limits. The description carries the full burden but remains somewhat incomplete.

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 concise sentence that effectively communicates the tool's purpose and key details without extraneous information. Every word 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 one parameter and no output schema, the description adequately covers what the tool returns (risks, stage, milestones, contracts). It could mention output format or error handling, but for a simple get-by-slug tool, 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?

The input schema has 100% coverage with a clear description for 'slug'. The description adds 'by slug' and lists return fields, confirming the parameter's role. While schema alone suffices, the additional context on output is beneficial but not transformative.

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 retrieves a 'Full project record by slug', specifying the resource (project record), method (by slug), and included fields (risks, stage, milestones, contracts). This distinguishes it from sibling tools like 'list_projects' (list all) and 'entity_profile' (possibly broader entity info).

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 when needing a complete project record identified by slug, but provides no explicit guidance on when not to use it or alternatives for partial data or different identifiers. Agents must infer context from the tool name and sibling names.

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?

Without annotations, the description must convey behavior. It indicates a read-only listing operation, which is clear. However, it does not mention potential size of the list, performance considerations, or any rate limiting, which would improve transparency.

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 with no extraneous words. It efficiently communicates the tool's purpose and scope.

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 no-parameter list tool with no output schema, the description is mostly complete. It could be improved by hinting at the return format (e.g., project IDs, names) or any limitations, but the information provided suffices for basic usage.

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 zero parameters, so schema coverage is 100%. Per guidelines, 0 parameters yields a baseline of 4. The description adds context about the project types listed, which is helpful beyond the empty 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 lists all L2BEAT-tracked projects and specifies types like rollups, validiums, optimiums, sidechains. The verb 'list' and resource 'projects' are unambiguous, and it distinguishes from sibling 'get_project' which focuses on a single entity.

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 obtaining a full list but does not explicitly state when to use this versus alternatives like 'get_project' or 'compare_entities'. No when-not-to-use or exclusion criteria are provided.

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 burden. It discloses rate limits (5 per identifier per day), that it's free, and that feedback affects roadmap. However, it doesn't specify data handling or side effects, but cover key behavioral traits.

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

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

The description is well-structured and front-loaded with purpose. While it is somewhat lengthy, every sentence provides valuable information without redundancy. Could be slightly more concise but still effective.

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

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

Given the tool has 3 parameters (including a nested object) and no output schema, the description is complete: it explains what each parameter does, when to use, constraints, and how feedback is processed. No gaps remain.

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 'type' enum in detail, noting that 'context' is optional but structured, and specifying message length (2000 chars) and content expectations.

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

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

The description clearly states the tool is for reporting bugs, missing features, data gaps, or praise to the Pipeworx team. It specifies the resource (Pipeworx team) and the action (tell), and distinguishes from sibling tools by focusing on feedback about tools/packs.

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 (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompt). It also mentions rate limits and that it's free, providing clear context for usage.

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

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

Annotations already provide readOnlyHint=true, and the description adds detailed behavioral context: it walks child markets, extracts dates/thresholds, sorts, and checks violations. 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?

The description is front-loaded with the main purpose, followed by explanation and usage. It is moderately verbose but each sentence adds value, and it is well organized.

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, the description fully explains the concept, input, logic, and output format. No output schema exists, but the return structure is described. Siblings are not mentioned, but the tool's specificity makes it 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%, so baseline is 3. The description restates the parameter's purpose and provides an example, adding some value but not significantly 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 via monotonicity violations, with concrete examples. It distinguishes from siblings by specifying its unique function of checking price ordering within a single event's nested markets.

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 tells agents when to use the tool (when they want arbitrage opportunities from a Polymarket event) but lacks explicit alternatives or when-not-to-use guidance. However, the purpose is unambiguous enough.

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

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

Annotations indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds detailed behavioral context: model sources (FRED, coinpaprika), scanning top markets, grouping by asset, single fetch of price history, ranking by edge magnitude, and trade direction suggestion. 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 a single paragraph that front-loads the core function followed by model details and purpose. Every sentence adds value, though length could be trimmed slightly without losing clarity.

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 no output schema, the description sufficiently explains the return (top N ranked by edge magnitude with suggested direction). Parameters are well-documented, and the model process is clear. Minor gap: exact output structure not specified.

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

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

Input schema covers 100% of parameters with descriptions. The description mentions 'Top N' for limit, 'volume window' for window, and 'minimum edge' for min_edge_pp, but adds minimal extra meaning beyond the schema. Baseline 3 is appropriate.

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

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

Description uses specific verbs ('scan', 'return') and resource ('Polymarket markets'), clearly stating it identifies edges where Pipeworx data disagrees with market price. It distinguishes itself from sibling tools like polymarket_arbitrage by targeting opportunity discovery without manual paging.

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 the tool is built for the 'what should I bet on today' question, providing clear context for when to use. It implies usage for discovering opportunities but does not explicitly exclude alternatives or state when not to use.

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 scoping to identifier and pairing with remember/forget. No annotations present, but description covers key behavioral aspects 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?

Two focused sentences with additional notes on scope and pairing. Purpose is front-loaded; could be slightly more concise but effective.

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

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

With one parameter and no annotations, the description explains retrieval behavior, listing capability, and scope. Adequate for tool 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 covers the sole parameter with description. Description reiterates same info (omit key to list), adding no new meaning beyond schema.

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

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

The description clearly states the tool retrieves a saved value or lists keys, distinguishing it from siblings 'remember' and 'forget'.

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

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

Provides specific examples like user's target ticker or prior notes, indicating when to use recall to avoid re-deriving context. Lacks explicit when-not-to-use but 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?

With no annotations, the description carries the full burden. It reveals parallel fan-out to multiple sources, date format options, and return structure. Missing are side effects, rate limits, or data freshness 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 well-structured with examples, but slightly verbose. Each sentence adds value; no waste.

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 3 parameters and no output schema, the description covers purpose, usage, input formats, data sources, and output structure. Missing edge cases like empty results or error handling.

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 3). The description adds meaning beyond schema: 'type' limited to company, 'since' examples with ISO and relative shorthand, 'value' ticker or CIK format.

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 find recent changes about a company, with specific query examples. It differentiates from siblings like 'activity' by detailing the data sources (SEC, GDELT, USPTO) and output structure.

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 query examples and mentions monitoring use cases. However, it lacks guidance on when not to use it or alternatives among siblings.

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?

Full burden on description due to no annotations. Discloses scoping, persistence (authenticated vs anonymous), and key-value storage. No contradictions.

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

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

Concise yet thorough, front-loaded with purpose, every sentence adds value.

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

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

Covers usage, storage mechanism, scoping, persistence, and sibling tools. Complete for a simple store operation.

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 100% so baseline 3. Description adds examples for key and value types, enhancing meaning 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 'Save data the agent will need to reuse later' and provides specific examples. Distinguishes from siblings by mentioning 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 (discover something worth carrying forward) and scoping details. Lacks explicit when-not but adequately guides usage.

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

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

With no annotations, description must fully disclose behavior. It explains it returns IDs and citation URIs but does not explicitly state it is read-only or mention any side effects, auth needs, or rate limits. Adequate but not rich.

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?

Information is front-loaded with purpose and examples. Concise at 4-5 sentences with no fluff, though could be slightly more compact by combining some 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?

Covers what the tool does, what IDs it returns, usage order, and example results. Lacks mention of error handling or missing entities, but overall sufficiently complete for a lookup tool with no 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?

Schema coverage is 100% and schema descriptions already detail the parameters. The description repeats similar examples but does not add significant new meaning beyond the schema. Baseline 3 is appropriate.

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

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

Description clearly states the action (look up canonical/official identifier) and specific resources (company, drug). Examples with identifiers (CIK, ticker, RxCUI, LEI) further clarify scope and distinguish from sibling tools like compare_entities or entity_profile.

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

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

Explicitly states when to use ('when a user mentions a name and you need...') and the order of operations ('Use this BEFORE calling other tools'). Also mentions it replaces multiple lookup calls, providing strong 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?

No annotations exist, so the description carries the full burden. It discloses the time scope (today) and breakdown categories but does not specify whether the tool is read-only, what the output structure is, or any other behavioral traits like rate limits or auth requirements.

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 (one sentence) but lacks structure. It does not front-load the primary action or provide a clear subject-verb-object pattern. While not verbose, it sacrifices clarity for brevity.

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 (1 optional param, no nested objects, no output schema), the description is minimally adequate. It covers the what and when but omits details such as the shape of the result or how the breakdown is returned.

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 single optional 'slug' parameter described as 'Specific project (default: all)'. The description adds no additional meaning beyond the schema—it reinforces the project context but doesn't clarify format or 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 the tool provides a 'TVS breakdown by token category and project today.' It identifies the resource (TVS breakdown), the grouping dimensions (token category and project), and the temporal scope (today). This distinguishes it from siblings like tvs_history, though not explicitly.

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?

Implied usage context: provides a snapshot for today, so use when you need current TVS breakdown. No explicit when-not or alternatives mentioned. The sibling tvs_history likely handles historical data, but no guidance is provided.

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, and the description only states what the tool returns. It does not disclose any behavioral traits such as whether it performs computation, reads data, or has side effects. The agent cannot infer safety or constraints.

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 short sentence, which is concise. It front-loads the core purpose without unnecessary words. However, it could be slightly more informative without increasing length significantly.

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?

There is no output schema, so the description should hint at the structure of the returned time series (e.g., timestamps, values). It does not. Also, given sibling tools, it lacks differentiation. The description is incomplete for a information retrieval tool.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (slug and range). The description adds no additional meaning beyond the schema, meeting the baseline but not compensating for any gaps.

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 'Historical TVS time series' names the resource (TVS time series) and indicates historical data, but 'TVS' is an unexplained acronym, and it doesn't distinguish from sibling tool 'tvs_breakdown'. The purpose is vaguely clear but not specific.

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 tvs_breakdown or activity. There is no mention of use cases, prerequisites, or exclusions.

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

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

With no annotations provided, the description carries the full burden. It discloses the return format (verdict, structured form, actual value with citation, percent delta) and data source (SEC EDGAR + XBRL). It also notes it replaces multiple sequential calls. No destructive behavior is implied, and the description adequately characterizes 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 a single paragraph that opens with the main purpose, followed by usage guidance, scope, and output details. It is concise without being overly terse, though it could be slightly more structured with bullet points for the output format.

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 a single required parameter, no output schema, and no annotations, the description covers the tool's purpose, input, output format, and domain. It mentions the data source and the fact that it replaces multiple calls. There is no output schema to expand upon, but the description provides enough context for an agent to understand what the tool does and what it returns.

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?

There is only one parameter (claim) with 100% schema description coverage. The schema already describes it as 'Natural-language factual claim'. The tool description adds example values ("Apple's FY2024 revenue was $400 billion"), which is helpful but does not provide new semantics beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Fact-check, verify, validate, or confirm/refute a natural-language factual claim'. It uses specific verbs and resource, and provides example usage patterns like 'Is it true that…?', distinguishing it from siblings such as compare_entities or resolve_entity.

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: 'Use when an agent needs to check whether something a user said is true', with concrete examples. It also scopes the supported domain to company-financial claims. However, it does not mention when not to use it or list alternative sibling tools, which would make it a 5.

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