Nasa Power

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint as false. The description adds value by noting that Anthropic probing requires a BYO API key and that the user pays Anthropic directly – a key behavioral nuance beyond annotations. No contradictions.

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

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

The description is three sentences, each with distinct value: purpose, parameter clarification, and use cases. No filler or repetition. Front-loaded with the primary action and output.

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

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

Given no output schema, the description succinctly describes return structure ('per-model {score, confidence, signals, raw_response} + combined view'). It covers all parameters, use cases, and cost implications. Minor omission: no detail on scoring methodology, but acceptable for clarity.

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 meaning: it explains the default model for 'models', that '_apiKey' is only needed for Anthropic, and that 'context' disambiguates entities. This surpasses the schema's brief descriptions.

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

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

The description starts with a specific verb ('Probe') and resource ('LLMs'), clearly stating it scores visibility per model. It distinguishes from siblings like 'ask_pipeworx' (which likely replies as the entity) and 'scan_competitor_ai_presence' (broader). The mention of default model and optional Anthropic clarifies 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 lists three concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') providing clear context. It does not explicitly exclude alternatives or state when not to use, but the use cases are specific enough to guide 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?

No annotations provided, so description carries full burden. Discloses routing across 300+ sources and automatic argument filling. Lacks mention of error handling or behavior when no source matches, but overall gives good behavioral overview.

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?

All sentences add value: purpose, use-case, examples. Well-structured, no wasted words.

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

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

Simple tool with one param and no output schema. Description covers purpose, usage, and data sources comprehensively. Could mention output format, but not essential for selection.

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 describes 'question' as natural language; description adds concrete examples ('What is the US trade deficit with China?'), adding value beyond schema. Baseline 3 due to 100% schema coverage, but examples elevate it.

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

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

Clearly states the tool answers natural-language questions by automatically selecting the right data source. Lists numerous examples and distinguishes from siblings (specific data tools) by being a meta-routing tool.

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

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

Explicitly states when to use: when user asks certain types of questions and you don't want to pick the specific tool. Implies alternatives are the sibling tools, and provides usage examples.

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

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

The description explains the internal process (market resolution, classification, fan-out to packs) and output format, adding value beyond the annotations which already indicate read-only and non-destructive 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 well-structured with each sentence adding value, though it is somewhat verbose; still appropriate for the tool's complexity.

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

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

Given no output schema, the description adequately explains the return type (evidence packet + market-vs-model comparison) and covers the tool's core functionality, making it complete 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 coverage is 100%, and the description reinforces the schema examples but does not add new semantic meaning beyond what the parameter descriptions already provide.

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

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

The description explicitly states it researches a Polymarket bet by pulling Pipeworx data, and distinguishes itself from siblings by being the 'core demo product' that handles the fan-out automatically.

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?

It clearly lists example queries for when to use the tool, but does not explicitly mention when not to use it or name alternatives, though it implies this is the preferred high-level tool.

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

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

With no annotations, the description carries full burden but only states the tool's output type. It omits any behavioral traits such as data source, time aggregation, error handling, or required permissions.

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 single sentence is too brief; it under-specifies essential details. True conciseness would front-load key info while still providing necessary context, which is absent here.

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 4 parameters, no output schema, and no annotations, the description is grossly incomplete. It does not cover parameter roles, return format, or any usage constraints, making it insufficient for reliable tool invocation.

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

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

Schema description coverage is 0%, and the description fails to explain any parameters (latitude, longitude, community, parameters). No meaning is added beyond the schema field names, leaving agents guessing about required 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 states the tool returns 'long-term monthly climatology averages for a coordinate,' which is a specific verb+resource. It differentiates from siblings like point_data and regional_data by focusing on climatology averages, but doesn't explicitly contrast them.

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 information is provided about when to use this tool versus alternatives, nor any indications of prerequisites or limitations. The agent receives no guidance on appropriate contexts.

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?

Describes data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/ClinicalTrials for drugs) and return type (paired data + citation URIs). No annotations provided, so description carries full burden. Lacks details on response structure or potential errors, but is adequate for basic understanding.

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 at 5-6 sentences, each adding value. Front-loaded with purpose, then use cases, data sources, return info, and efficiency note. No redundant or missing 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?

Covers usage, data sources, and constraints (2-5 items). With no output schema, the return description ('paired data + citation URIs') is somewhat vague but acceptable given complexity. Sibling tools provide context for single-entity queries.

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

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

Schema covers both parameters (type, values) with descriptions. The description adds significant meaning: explains how to format values based on type (tickers/CIKs for company, names for drug) and what data each type retrieves. This goes beyond the schema enum and array 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 compares 2-5 companies or drugs side by side, with specific verbs and resources. It distinguishes from sibling entity_profile by focusing on multiple entities. Use cases are listed (e.g., 'compare X and Y', 'X vs Y').

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

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

Explicitly says when to use: when user says 'compare X and Y' etc., and notes it replaces 8-15 sequential calls. Does not explicitly state when not to use, but the context is clear enough for an agent to infer alternatives like entity_profile for single entities.

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?

Describes return type (names+descriptions) but doesn't mention if it's read-only or any side effects. No annotations provided, so description carries burden; it's mostly transparent but missing explicit safety note.

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 focused sentences: purpose, usage guidance, and behavior. No wasted words, front-loaded with key action.

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 explains what is returned (top-N tools with names+descriptions). Complete for a discovery tool.

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

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

Schema has 100% coverage with descriptions for both 'query' and 'limit'. Description adds context about returning top-N, reinforcing limit parameter's purpose.

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

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

Clearly states it finds tools by describing data/task, and returns top-N relevant tools. Distinct from sibling tools which are domain-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?

Explicitly says when to use (browse/search/discover) and lists example domains. Recommends calling it FIRST when many tools available.

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. It discloses returned data types (SEC filings, fundamentals, patents, news, LEI) and citation URIs. Could explicitly state read-only nature, but implied by 'Get'.

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 multi-sentence but each sentence adds unique value (purpose, examples, data sources, param guidance, alternative). Front-loaded with core purpose. Could be slightly more terse but appropriate for complexity.

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

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

Given no output schema, the description lists return types comprehensively. It covers input handling, sources, and usage context. Agent can invoke correctly without ambiguity.

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?

Both parameters are described in schema (100% coverage). Description adds: type limited to 'company' (with note on future support), value can be ticker or CIK, and guidance to use resolve_entity for names.

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 'Get everything about a company in one call' and gives example phrasings. It distinguishes from sibling tools like resolve_entity (name resolution) and compare_entities (comparison).

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

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

Explicitly says when to use: when user asks 'tell me about X', or when multiple tools would be needed. Also specifies when not: names not supported, use resolve_entity first.

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, but description clearly indicates destructive action (delete). Could mention irreversibility or no confirmation, but adequate for simple case.

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

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

Two concise sentences with front-loaded purpose and usage guidance. No fluff.

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?

Complete for a simple tool: 1 param, no output schema, no nested objects. Description covers purpose, usage, and sibling context.

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

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

Schema covers 100% of parameter (key) with description. Description adds no extra meaning beyond schema. 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?

Clearly states 'Delete a previously stored memory by key' with specific verb and resource. Distinguishes from siblings 'remember' and 'recall' which handle storing and retrieving.

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...'. Also suggests pairing with remember and recall.

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 safe, read-only behavior (readOnlyHint, openWorldHint, idempotentHint all true, destructiveHint false). The description adds valuable behavioral context: it fetches the page, extracts title/description/key links, and emits standard markdown format. 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 concise, front-loaded with the main purpose, and each sentence adds value. It includes usage examples without 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 simplicity (2 params, no output schema), the description fully covers what the tool does and what it returns ('single text blob in standard llms.txt markdown format'). Combined with clear annotations, it is complete enough 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 coverage is 100%, so baseline is 3. The description does not add significant meaning beyond the schema; it implicitly references the 'url' parameter but provides no additional detail on 'max_links' beyond the schema's description.

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

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

The description clearly states the tool generates an llms.txt file for a URL, specifying the verb 'generate', the resource 'llms.txt file', and the purpose for AI crawler indexing. It distinguishes from siblings like 'ai_visibility_check' by explicitly listing use cases (client sites, own projects, competitor auditing).

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 for when to use the tool, including three specific use cases (client indexing, drafting, auditing). However, it does not explicitly mention when not to use it or name 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?

Discloses rate limiting, daily human review, and roadmap impact despite lacking annotations. No contradictions. Doesn't describe after-submission behavior but that's minor for a feedback 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?

Three sentences, efficient, front-loaded with purpose then specifics. Every sentence adds value.

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

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

Given no output schema, description covers usage limits, content expectations, and team handling. Adequately complete for a feedback tool; minor gap on response format.

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

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

Schema already covers all parameters (100% coverage). Description adds value by explaining 'message' formatting guidelines and that 'context' is optional, slightly improving over schema alone.

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 identifies the tool as a feedback mechanism for Pipeworx team, specifying types of feedback (bug, feature, data_gap, praise). Distinct from sibling tools that focus on data retrieval or analysis.

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

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

Explicitly states when to use (tool returns wrong data, tool missing, positive experience) and provides instructions on how to format feedback. Also mentions rate limits (5 per day) and cost (free).

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds substantial behavioral context: data source (CF analytics-engine), PII-free nature, and caching window (5min-1h). This goes well beyond what annotations provide, fully informing the agent of safety and latency.

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

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

Two concise paragraphs: first states what the tool does and the parameter options; second lists three concrete use cases and implementation details. Every sentence is necessary and front-loaded. No redundancy or fluff.

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 enum parameter, no output schema), the description covers purpose, usage guidance, parameter semantics, and behavioral transparency. It lacks a precise return format description, but the mention of 'top tools, top packs, total call volume' provides enough context for a read-only trending 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% (the 'window' parameter is fully documented). The description adds practical guidance on the effect of different values: 'shorter windows surface what's hot right now; longer windows show steady-state demand.' This adds meaning beyond the schema's enum listing.

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

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

The description clearly states that the tool returns trending tools, packs, and call volume over specified windows. It defines the resource ('top tools, top packs, total call volume') and the verb ('returns'), distinguishing it from sibling tools like 'ask_pipeworx' and 'discover_tools' by focusing on aggregated usage trends.

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 three explicit use cases (discovering hot data sources, confirming canonical tools, aligning use case with demand) and mentions caching behavior. It does not explicitly state when not to use it or compare directly to siblings, but the context is clear enough for typical 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?

No annotations are provided, so the description carries full burden. It discloses that temporal granularity is controlled by dates and daily by default, but it does not mention data source, limitations, or what the returned observations contain (e.g., default parameters, units).

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 two sentences, front-loaded with the main purpose, and contains no unnecessary words or information.

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

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

Given 7 parameters and no output schema, the description is incomplete. It does not explain what the time-series observations include (e.g., default parameter values, number of data points, handling of missing data) or how the output is structured.

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 that dates control granularity and have YYYYMMDD format (though schema already describes format), but it does not mention other optional parameters like temporal, community, or parameters.

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 'Time-series observations for a single coordinate', which is a specific verb+resource. This distinguishes it from siblings like 'regional_data' (for areas) and 'climatology' (for long-term averages).

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 time-series data at a point and explains date format and default granularity, but it does not explicitly state when to use this tool over alternatives like 'regional_data' or 'climatology'.

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

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

The description goes beyond the annotations (readOnlyHint=true, destructiveHint=false) by detailing the tool's behavior: it 'walks the child markets, extracts dates/thresholds, sorts them, and reports any pair where the rule is violated.' It also describes the return format. There is 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 a single, well-structured paragraph that efficiently communicates the purpose, rationale, process, and output. Every sentence adds value without 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 simplicity (one parameter, no output schema), the description is complete. It explains the input, the logic, and the output format ('list of {market_a, market_b, gap_pp, suggested_trade} entries'), leaving no 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?

The input schema has 100% description coverage for the single parameter 'event,' which is already documented in the schema. The description adds context about how the tool uses the parameter but does not provide additional semantic meaning beyond what the schema offers.

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: 'Find arbitrage opportunities within a Polymarket event by checking for monotonicity violations.' It specifies the verb (Find) and resource (arbitrage opportunities), and provides a detailed explanation that distinguishes it from sibling tools.

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

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

The description tells users to 'Pass a Polymarket event slug or URL' and explains the condition under which to use the tool (when event has multiple 'by date' or 'by threshold' markets). It provides clear context but does not explicitly state when not to use it or mention 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?

Beyond annotations (readOnlyHint, openWorldHint), the description details the algorithm: V1 covers crypto-price bets, uses lognormal model from FRED and live coinpaprika price, groups by asset, fetches price history once, computes model probability, ranks by edge, and returns suggested trade direction. 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 slightly verbose but each sentence adds value: purpose, method, limitations (V1), and use case. It front-loads the main purpose. Minor redundancy could be trimmed.

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

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

Given no output schema, the description fully explains returns (top N ranked by edge magnitude with suggested trade direction) and covers tool complexity: scanning, grouping, model computation, ranking. It also notes limitations (V1 covers crypto-price bets).

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 clear parameter descriptions. The description does not add new meaning beyond the schema (e.g., limit, window, min_edge_pp are adequately explained). 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 scans high-volume Polymarket markets, finds where Pipeworx data disagrees with market price, and returns top N ranked by edge magnitude. It uses specific verbs ('scan', 'return') and resource ('Polymarket markets'), distinguishing it from siblings like 'polymarket_arbitrage'.

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

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

The description specifies the tool is built for 'what should I bet on today' and helps discover opportunities without manual paging. It implies usage context but does not explicitly exclude scenarios or reference alternative tools.

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

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

Annotations already indicate safe read-only, idempotent, non-destructive behavior. The description adds value by detailing the spread calculation (Kalshi minus Polymarket in percentage points) and the typical spread range (2-25pp). It also describes the return structure without contradicting 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 detailed yet concise, front-loading the main purpose and then structuring information about modes and return values. Every sentence adds value without 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 lack of an output schema, the description adequately describes return values (leg-by-leg prices and spread in percentage points). The three parameters are fully covered in schema and description, making the tool's behavior completely understandable.

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 all three parameters. The description adds significant meaning by explaining the two modes, how parameters interact (overrides), and provides concrete examples for event ticker and slug, enriching the schema information.

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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question, naming specific venues and explaining the arbitrage signal. It distinguishes from siblings like 'polymarket_arbitrage' by explicitly mentioning both venues and the spread calculation.

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 explains two modes (topic and explicit) with clear guidance on when to use each. It provides examples for explicit parameters. However, it does not explicitly tell when to use this tool over other sibling tools like 'polymarket_arbitrage', which is a minor gap.

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, so description carries full burden. Discloses scoping by identifier and pairing with remember/forget. Does not detail error handling or output format, but sufficient for a read-only retrieval 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?

Three sentences, front-loaded with action, no redundant information. 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?

With one optional parameter, no output schema, and no annotations, the description covers usage, scoping, and pairing with sibling tools adequately. Complete for its simplicity.

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

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

Schema already describes the key parameter completely (100% coverage). Description adds context: omitting key lists all keys, and explains the role of the key in retrieving stored information.

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 retrieves a value saved via remember or lists all keys, with specific verb 'retrieve' and resource 'memory value'. It distinguishes from sibling tools 'remember' and 'forget' by mentioning pairing.

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

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

Explicitly says when to use (look up stored context) and when not to re-derive. Mentions scoping and pairing. Slightly less explicit about not using when irrelevant or 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?

With no annotations, the description fully discloses behavior: it fans out to multiple sources (SEC EDGAR, GDELT, USPTO) and returns structured results with citations. It does not mention side effects, but as a query tool, the read-only nature is implied.

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 well-structured: starts with purpose, provides usage examples, explains the fan-out mechanism, parameter details, and return format. Every sentence adds value, and there is no extraneous information.

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

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

Given the lack of output schema, the description adequately explains the return format (structured changes, count, URIs) and the data sources. It could mention potential limitations (e.g., max date range) or error handling, but it is sufficient for a straightforward 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?

The input schema has 100% coverage, but the description adds valuable context: explains 'since' accepts ISO dates or relative shorthand with examples, specifies that 'type' only supports 'company', and clarifies 'value' accepts ticker or CIK. This enriches the schema without repetition.

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 retrieve recent changes for a company within a specified time window. It provides explicit use case queries ('What's happening with X?', 'any updates on Y?') and distinguishes itself from siblings by focusing on monitoring changes across multiple sources.

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

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

The description gives clear examples of when to use the tool, such as monitoring updates or briefing on recent activity. It does not explicitly state when not to use it or compare to sibling tools, but the context is sufficient for typical scenarios.

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 for behavioral disclosure. It mentions a bounding box area constraint but does not disclose the nature of returned data, error handling, rate limits, authentication needs, or side effects. This is insufficient for a query 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 sentence that efficiently conveys the core purpose and a key constraint. It is front-loaded and contains no superfluous 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 lack of output schema and 9 parameters with low coverage, the description provides the essential spatial and temporal context but omits details on community/parameters and result format. It is adequate for basic usage but incomplete for complex queries.

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 description adds meaning for spatial parameters by indicating they form a bounding box, and for temporal parameters by implying they set the date range. However, with only 33% schema description coverage and no explanation for 'community' or 'parameters', it only partially compensates for schema 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 clearly states it is a bounding-box query for daily or monthly data over a rectangular region, with a specific area limit. This distinguishes it from siblings like point_data or climatology by specifying spatial aggregation and temporal granularity.

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 explicit guidance on when to use this tool versus alternatives, nor any conditions for use or exclusion criteria. It only states what it does, missing information about prerequisites or when to prefer other 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?

No annotations exist, so description carries full burden. It discloses memory persistence: authenticated users get permanent, anonymous sessions 24 hours. Does not mention deletion constraints or side effects. Adequate for a store operation.

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

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

Four sentences, each adding distinct value. Front-loaded with main purpose. No redundancy or filler.

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

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

With no output schema, description does not explain return value, but as a store operation, return is likely minimal. All essential aspects (purpose, usage, persistence) covered. Slightly missing side effects or limitations.

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. Description adds example keys and confirms value is text, but schema already provides type. No significant additional meaning beyond schema examples. Baseline 3.

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

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

Clearly states 'Save data the agent will need to reuse later', specifying verb (save) and resource (data). Distinguishes from sibling tools recall and forget by mentioning pairing. Provides scope: key-value per agent identifier.

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 you discover something worth carrying forward... so you don't have to look it up again'. Suggests pairing with recall and forget but doesn't exclude when not to use (e.g., sensitive data). Good directional 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 are provided, so the description bears full responsibility. It discloses that the tool returns IDs plus citation URIs, but does not mention any behavioral aspects like side effects, rate limits, or authentication requirements. For a read-only lookup tool, this is minimally adequate but could be more 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 (3 sentences), starts with the core purpose, includes concrete examples, and ends with usage guidance. Every sentence adds value, and the structure is clear and 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 that there is no output schema, the description adequately explains return values (IDs plus pipeworx:// citation URIs) and covers input examples and usage order. It is complete enough for an agent to understand how and when to use the tool 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 coverage is 100%, and the description does not add new parameter details beyond what the schema already provides. The schema already explains the 'value' parameter's allowed formats. Since the schema is comprehensive, the description doesn't need to add more, but 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: to look up canonical/official identifiers for companies or drugs, listing specific ID systems (CIK, ticker, RxCUI, LEI) and the contexts where it should be used. It differentiates from sibling tools by advising to use it before other tools that need official identifiers.

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

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

The description provides explicit usage guidance: use when a user mentions a name and you need identifiers that other tools require. It also states to use it before calling other tools and mentions it replaces 2-3 lookup calls. However, it does not explicitly mention when not to use it or provide alternative tools.

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

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

Annotations declare readOnlyHint, idempotentHint, and no destructiveness. The description adds value by detailing the internal probe mechanism, ranking, and output fields (score, confidence, signal density), going beyond annotations.

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

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

Three concise sentences front-load the main purpose and behavior, with no unnecessary words. Every sentence provides 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?

Despite no output schema, the description specifies return fields (score, confidence, signal density). Parameters are fully documented, and annotations cover side effects, making the tool's behavior complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra meaning by noting the first entity is treated as the 'subject' and that context disambiguates names, which enhances understanding 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 explicitly states the tool compares AI visibility across entities, probes each with ai_visibility_check, ranks results, and surfaces most/least recognized. It distinguishes from siblings like ai_visibility_check (used internally) and compare_entities by focusing on competitive audits.

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 clear usage context (competitive AI-marketing audits) and an example question. However, it lacks explicit when-not-to-use or direct alternative comparisons 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?

No annotations provided, so description carries full burden. It discloses the return format (verdict, structured form, actual value with citation, percent delta) and mentions it replaces multiple calls. Lacks details on rate limits or error handling but is fairly 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 and well-structured. Each sentence adds value: it states purpose, provides usage examples, specifies domain, describes output, and highlights efficiency gains. No redundant information.

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

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

Given the tool has one parameter and no output schema, the description is complete. It explains the input, output, domain constraints, and even mentions the underlying data source (SEC EDGAR + XBRL).

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

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

Schema has 100% coverage for the single parameter 'claim'. The description adds meaning by explaining the type of input (natural-language factual claim) and providing examples, going beyond the schema's basic description.

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

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

The description clearly states the tool's purpose: fact-check, verify, validate factual claims. It uses specific verbs and resources, and distinguishes itself from siblings by noting it replaces multiple sequential calls.

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

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

The description provides clear usage guidance: when to use (checking truth of claims) and gives example queries. It also specifies the domain (company-financial claims for US public companies) but does not explicitly state when not to use or mention alternatives.

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