Horizons Nasa

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds valuable behavioral details: scoring per model (0-100), return structure (per-model and combined view), and API key handling (passed straight through). 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 a single, well-structured paragraph. It front-loads the main purpose, then efficiently covers model options, output details, and use cases. 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?

The tool has 4 parameters (all described in schema), no output schema, but the description fully explains return values (per-model {score, confidence, signals, raw_response} + combined view). It covers input, output, and use cases comprehensively, leaving no significant gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning beyond schema by specifying the default model (Workers AI Llama-3.3-70b, free) and clarifying _apiKey usage (BYO key, direct payment to Anthropic). This enhances agent understanding of cost and model selection.

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 probes LLMs for knowledge about entities and returns visibility scores (0-100). It uses specific verbs ('probe', 'score') and resources ('business/brand/product/topic'), effectively distinguishing it from sibling tools like 'ask_pipeworx' or 'scan_competitor_ai_presence'.

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 use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains prerequisites (default model free, _apiKey needed for Anthropic). While it doesn't explicitly state when not to use, the context is sufficient for an agent to decide.

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 false. The description adds that it returns a structured answer with stable citation URIs, providing useful context beyond the annotations.

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

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

The description is well-structured and front-loaded with the key message, but it is somewhat lengthy. However, every sentence adds value, so it is appropriately sized for the complexity.

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

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

Given no output schema, the description provides sufficient context about the return format and usage scope. It covers alternatives, examples, and the tool's role as a default entry point.

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

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

Schema coverage is 100%, so the baseline is 3. The description provides examples but does not add new semantic meaning beyond what the schema already covers for 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 is for factual, authoritative queries, lists many domains, and explicitly distinguishes from sibling tools like ask_pipeworx_grounded and deep_research.

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

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

Explicitly says 'PREFER OVER WEB SEARCH', provides a list of use cases, and tells when to step up to alternatives with concrete 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?

Annotations already indicate safe read-only behavior. Description adds critical details: refusal reasons list, exact return format, routing mechanism, and the fact that answers are extracted only from tool results. This goes well 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?

Single paragraph that front-loads the core purpose, then explains behavior, usage, and tradeoff. Every sentence adds unique value with no redundancy.

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

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

Despite no output schema, the description fully specifies the return structure and possible refusal reasons. Combined with clear parameter schema and annotations, this gives an agent everything needed to 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 description coverage is 100% with all parameters being aliases for 'question'. The description mentions the question implicitly but adds no extra meaning beyond the schema. Baseline 3 is appropriate as schema handles parameter semantics.

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

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

The description clearly states the tool as a 'hallucination-resistant answer mode for high-stakes reads' and contrasts it with sibling tool 'ask_pipeworx' for casual lookups. It specifies verb (ask, extract, return) and resource (tool results from 4,774 tools across 1,242 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?

Explicitly tells when to use (answers to be quoted or acted on, high-stakes domains like legal/medical) and when not to (prefer ask_pipeworx for casual lookups). Also mentions cost tradeoff (one extra LLM call).

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, destructiveHint=false. Description adds extensive behavioral details: fan-out logic, resolver contract, parent_event extractor, news fallback, safety mechanisms (low-confidence short-circuit, closed markets, wide-spread warnings), and resolution-rule risk. 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 lengthy (~80 lines) but well-structured with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). Every sentence provides essential information; however, length may slightly reduce scanning efficiency for an AI agent.

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 high complexity (4 classifiers, many data sources, response shapes, safety mechanisms, resolution rules) and no output schema, description is thoroughly complete. It covers all key aspects: market resolution, evidence sources, response structure, edge cases, and interpretation guidance.

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

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

Schema coverage is 100%; description adds meaning beyond schema by explaining market input types (slug, URL, question), depth options with concrete examples (quick=2-3 sources, thorough=full fan-out), and include_raw usage with size implications. All three parameters are well-documented.

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 states a specific verb ('Research') and resource ('Polymarket bet'), clearly distinguishing from siblings like polymarket_edges and polymarket_arbitrage. It explicitly lists input types and output components.

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 when to use: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' No explicit when-not or alternatives, but context implies specialized research tool.

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

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

Annotations already declare the tool is read-only, open-world, idempotent, and non-destructive. The description adds valuable behavioral details beyond annotations: off-calendar fiscal year handling (e.g., AAPL Sep, NVDA Jan), result sorting by primary metric, and inclusion of citation URIs. No contradictions.

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

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

The description is moderately long but tightly packed with useful information. It front-loads example queries, which is effective. Every sentence contributes value, though a slight reduction in length could improve conciseness 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?

Given the tool has only 2 parameters and no output schema, the description provides comprehensive context: what data is pulled for each type, how results are sorted, what return format includes (paired data + URIs), and its efficiency advantage. It leaves no major gaps for an agent to infer.

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 parameters are already well-defined. The description adds context by providing examples of valid values (tickers for company, drug names like 'ozempic') and reiterating the min/max constraints. It also explains how the type parameter determines the data source, which adds meaning beyond the schema's enum 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 explicitly states the tool does side-by-side comparison of 2–5 companies or drugs, lists example queries like 'X vs Y' and 'which is bigger', and distinguishes between two entity types with specific data sources (SEC EDGAR for companies, FAERS for drugs). It clearly differentiates from sequential lookups, making the purpose unmistakable.

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 strongly recommends preferring this tool over sequential single-pack lookups, with 'ALWAYS PREFER' in bold. It provides clear context for when to use it. However, it does not explicitly discuss limitations or alternatives (e.g., when not to use it), which would elevate it to a 5.

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 readOnly, idempotent, non-destructive. The description adds context: it runs parallel tools, returns findings with gaps, never invents, and expects 15-60s latency. 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?

Dense but every sentence adds value. Structured with auth info first, then core functionality, output format, use cases, and timing. Front-loaded with critical info.

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 format (findings packet with evidence, confidence, source, gaps). Covers complexity, constraints, and distinguishes from siblings. Complete for the tool's sophistication.

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 description adds meaning: question should be broad/multi-part, depth maps to number of facets (quick=3, etc.), and highlights that 'thorough' requires paid plan.

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: grounded multi-source research over structured data, not open-web search. It distinguishes itself from sibling tools like ask_pipeworx by specifying that it decomposes questions into facets and returns evidence with gaps.

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 provides when to use (broad/multi-part structured queries) and when not (single lookup: use ask_pipeworx; breaking news: prefer ask_pipeworx). Also specifies account requirements and alternative for unauthenticated users.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior, so no contradiction. The description adds value by disclosing that the output includes 'names, descriptions, and full input schemas (with curated examples)' and that results are ready to call directly. This transparently sets expectations about the tool's behavior and return format.

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 yet complete, with a logical structure: action, usage context, domain list, output description, and a strong directive. Every sentence serves a purpose without redundancy. It is front-loaded with the core action and guidance.

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

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

Given the absence of an output schema, the description adequately explains what the tool returns: top-N relevant tools with schemas and examples. It covers the input parameters (via schema) and the overall use case. The agent has enough information to invoke the tool correctly and interpret results, making it contextually 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 description coverage is 100%, so the baseline is 3. The description provides natural language examples (e.g., 'look up FDA drug approvals') that add meaning beyond the schema's parameter descriptions. However, it does not significantly elaborate on the 'limit' parameter beyond what is in the schema. Overall, it adds context for the main 'query' parameter.

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

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

The description clearly states the verb ('Find tools') and the resource ('by describing the data or task'). It distinguishes from sibling tools by specifying that it is for browsing/discovering the option set, not for executing specific lookups like 'lookup' or 'search_within'. The list of supported domains further 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?

Provides explicit guidance: 'Call this FIRST when you have many tools available and want to see the option set' and 'Use when you need to browse, search, look up, or discover what tools exist for...'. It also explains the output is ready to call, eliminating the need for a second schema lookup, which informs optimal invocation order.

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?

Disclosed multiple behavioral traits: fans out across SEC/XBRL/USPTO/news/GLEIF, patents API sunset with soft-fail, GDELT→GNews fallback, pass ticker or CIK, zero-padded CIK required, names not supported. Annotations already indicate readOnly/idempotent, so description adds valuable context beyond annotations.

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

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

Description is a single dense paragraph but front-loads with example queries. Every sentence adds necessary detail. Could benefit from bullet points for readability, but efficient and well-structured for the information density.

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 (multiple sources, fallbacks, sunset), the description covers all essential aspects: expected inputs, return fields with specific details, failure modes, and limitations. No output schema exists, but description compensates adequately.

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. Description adds value by reinforcing that type must be 'company', value can be ticker or CIK, and explicitly saying names not supported. Provides extra context on zero-padded CIK and sibling tool 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 'full cross-source profile of a US public company in ONE parallel call' and specifies data sources and returned fields. It distinguishes from siblings by recommending over chaining single-pack lookups and referencing resolve_entity for names.

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 (holistic view), when not to use (names not supported, use resolve_entity first), and gives example inputs. Provides clear alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds no behavioral context beyond the default, such as API dependences, rate limits, or data freshness. Given annotation coverage, the description's minimal additional value warrants a 3.

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 of 6 words, which is concise and front-loaded. However, it is somewhat terse and could benefit from a brief expansion to improve clarity without sacrificing conciseness.

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 (6 parameters, output schema exists), the description is minimal. The term 'ephemeris' may be obscure, and the description does not clarify the output format or typical usage. Output schema helps, but the description could be more informative.

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 provides full description coverage (100%) for all 6 parameters. The description adds no additional meaning beyond the default note about 'default observer table', which is already captured in the table_type parameter description. 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 verb 'Generate' and the resource 'ephemeris', with clarification that it is a 'default observer table'. However, it does not differentiate from sibling tools like 'vectors' or 'observers', which may serve similar purposes.

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

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

The description provides no guidance on when to use this tool versus alternatives. No context about prerequisites, exclusions, or typical use cases is given.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds no behavioral context beyond deleting by key, such as error handling when key doesn't exist, but does not contradict 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 two concise sentences with no fluff. First sentence states the action, second provides usage guidance and alternative tools, perfectly front-loaded.

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 delete operation with one parameter and no output schema, the description covers purpose, usage, and sibling context completely.

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

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

Schema coverage is 100% and the parameter 'key' is described in the schema as 'Memory key to delete'. The tool description merely says 'by key', adding no further meaning beyond the schema.

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

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

The description clearly states the action 'Delete a previously stored memory by key.' It explicitly distinguishes from sibling tools 'remember' and 'recall' by recommending pairing with them, avoiding confusion.

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 provides three specific when-to-use scenarios: stale context, task completion, clearing sensitive data. Also mentions sibling tools as alternatives, satisfying the requirement for explicit when and alternatives.

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

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

Annotations already indicate a safe, read-only, idempotent operation. The description adds value by detailing the process: fetches page, extracts title/description/links, and emits standard format. 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 well-structured, starting with purpose, then process, then use cases. It is slightly wordy but generally concise and front-loaded with key 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's simplicity (2 parameters, no output schema), the description adequately covers purpose, process, typical outputs, and use cases. No missing pieces for an agent to understand.

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% for both parameters (url, max_links). The description does not add additional meaning beyond the schema for parameters, 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 the tool generates a production-ready llms.txt file for any URL, specifying the action and resource. It distinguishes from siblings by being unique in its function.

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

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

The description lists three explicit use cases (indexing client sites, drafting for own projects, auditing competitors) which provide clear context for when to use the tool. However, it does not mention when not to use it or 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 declare readOnlyHint, idempotentHint, destructiveHint false, so the description's safety profile is clear. The description adds value by enumerating return fields, which is not in the schema (no output schema). 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?

Two sentences: first states purpose and return fields, second provides usage guidance. No unnecessary words, front-loaded with key information.

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

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

For a simple read-only tool with one optional parameter and clear annotations, the description covers purpose, return structure, and usage context. No output schema needed as fields are self-explanatory.

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

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

Schema coverage is 100% with a clear description for the only parameter (include_inactive). The tool description does not add additional meaning beyond the schema's parameter description. 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 it lists the caller's active subscriptions and specifies the return fields (id, type, params, created_at, last_fired_at, fire_count). It distinguishes from sibling tools subscribe and unsubscribe by mentioning usage before adding or to find an id to cancel.

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?

Explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use (before subscription actions) and provides context for retrieving IDs, effectively differentiating from subscribe/unsubscribe.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, covering safety and repeatability. The description 'Resolve an object name → SPK id' adds no behavioral context beyond what the annotations provide.

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 directly states the tool's purpose. No extraneous words 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 tool's simplicity and the presence of an output schema, the description is minimally adequate. However, it lacks details on error handling, return format, or edge cases, leaving some gaps for an agent.

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

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

Schema description coverage is 0%, so the description must compensate. It only vaguely states the transformation without specifying valid inputs, constraints, or format for the 'query' parameter (e.g., case sensitivity, allowed entities). This adds minimal meaning beyond the bare schema.

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

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

The description uses a specific verb 'resolve' and clearly states the resource transformation 'object name → SPK id'. It distinguishes itself from siblings like 'resolve_entity' and 'entity_profile' by focusing on a simple name-to-ID lookup.

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

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

No guidance is provided on when to use this tool vs. alternatives like 'resolve_entity'. There are no explicit when-to-use or when-not-to-use statements, leaving the agent without context for tool selection.

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

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

Annotations already declare the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds the behavioral detail that it is a convenience function with a fixed table_type. No additional behavioral traits (e.g., data scope, pagination) are disclosed.

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 of 6 words, front-loaded with 'Convenience' to immediately signal its purpose. Every word earns its place with 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?

Despite having an output schema, the description lacks essential context for parameters and what the tool actually returns. An agent unfamiliar with 'ephemeris' would need additional information to use this tool 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?

The schema has 0% description coverage, so the description carries the burden. It only clarifies that table_type is preset to OBSERVER. It does not explain the meaning or usage of the other parameters (command, start_time, stop_time, center, step_size), leaving significant ambiguity.

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 this tool is a convenience wrapper around 'ephemeris' with a preset table_type of OBSERVER. This specifies the resource (ephemeris data) and the operation (retrieve with observer type), distinguishing it from the full 'ephemeris' tool. However, it does not explicitly describe what observer data is, relying on the parent tool's context.

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

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

The description implies this tool is for quickly getting observer ephemeris without needing to specify table_type manually. It contrasts with 'ephemeris' by noting the preset parameter. However, it does not explicitly state when not to use it or provide alternatives beyond the parent 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?

Discloses rate limiting (5 per identifier per day), free usage, and that feedback affects roadmap. Annotations provide no extra hints, but the description fully covers 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?

Concise two-sentence description that front-loads purpose and usage, then adds constraints. Every sentence provides essential information 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 no output schema and simple feedback functionality, the description covers all necessary aspects: purpose, usage, rate limits, quota, and impact. It is complete for the tool's complexity.

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

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

Schema covers all parameters with descriptions. The description adds value by advising on specificity and format, complementing the schema's enum and structure details.

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

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

The description clearly states the tool's purpose: to send feedback about bugs, missing features, etc. It differentiates from siblings by focusing on feedback submission, which is unique among the listed tools.

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

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

Explicitly states when to use the tool (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompt). No alternative tools are needed as feedback is a standalone function.

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 data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h) beyond annotations. Annotations already declare readOnly, idempotent, non-destructive; description adds valuable context.

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?

Every sentence earns its place: result summary, use cases, technical details. Front-loaded with key output, 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?

Fully covers all aspects for a simple tool with one optional parameter. Annotations and description together provide complete behavioral and usage guidance.

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

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

Parameter description adds nuance about window semantics (shorter for hotness, longer for steady-state) beyond schema enum. Schema coverage is 100%, but description enriches understanding.

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

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

Description clearly states it returns top tools, packs, and call volume over recent windows, with specific verb 'returns' and resource 'trending on Pipeworx'. Distinguishes from siblings by focusing on trending data rather than queries or entity lookups.

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?

Lists three concrete use cases (discovering hot data, confirming canonical choice, seeing alignment) that guide when to use. Could explicitly state when not to use, but use cases implicitly exclude direct query tools like ask_pipeworx.

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 read-only, idempotent, non-destructive. The description adds deep behavioral context: internal algorithms (partition check, monotonicity check, semantic anchor with Jaccard similarity, partition filter, fill check with live CLOB depth), response structure, and conditions for valid trades. Very 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 lengthy but well-organized with clear sections for modes, filters, and response. Each sentence adds value. Slightly verbose but front-loaded with the core purpose, so agents can quickly grasp the tool's function.

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 thoroughly explains response fields (opportunities[], partition_check{}, fill check details) and provides actionable guidance (e.g., realizable_edge_pp ≤ 0 means don't trade). Missing explicit error handling or return type but sufficient for agent 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 coverage is 100% and the description adds rich meaning: explains each parameter's purpose, provides examples ('fed-decision-may-2026', 'Fed rate decision'), and describes the behavior in each mode. Goes well beyond schema definitions.

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 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks' and details two operational modes (trending scan, event, topic), clearly distinguishing from sibling tools like polymarket_edges and polymarket_fill_risk.

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 each mode (no args for trending, event for specific market, topic for cross-event), recommends 'event' for specific cases, and explains cross-event mode catches patterns single-event misses. Lacks explicit 'when not to use' but is clear 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 readOnly, idempotent, non-destructive. Description adds extensive behavioral details: model families (crypto_price, news_momentum, partition_overround, concentrated_longshot), edge calculation, caching 1h at KV level, diagnostics, and tradeable-edge knobs.

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?

Very detailed and well-structured with sections and bullet points, but overly verbose. Could be condensed while retaining key 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 9 parameters, no output schema, and complexity of models/filters, the description is exceptionally thorough, covering edge calculation, segment breakdown, diagnostics, and usage knobs.

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 all 9 parameters (100% coverage). Description adds significant value by explaining how parameters like min_partition_leg_kelly interact with others and providing default values and usage tips.

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 top Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, distinguishing it from sibling tools like polymarket_arbitrage and polymarket_kalshi_spread.

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 built for 'what should I bet on today' and mentions agents discover opportunities without paging hundreds of markets. Provides clear context but does not explicitly state when not to use 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?

Annotations provide readOnlyHint etc. Description adds significant behavioral context: decay from daily closes, 60-day TTL, gaps mean no scan. 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?

Well-structured, front-loaded purpose, then arguments and response. Slightly verbose but 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 description fully explains response fields (tracked, expired, snapshot_dates), limits, and computation details. Complete for AI understanding.

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 descriptions. Description adds defaults (days=14, window='1wk'), clamp range, and explains response fields, going beyond schema.

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

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

Description clearly states it provides edge persistence and decay telemetry from daily snapshots. It answers a specific question about edge age and trend, and distinguishes from sibling tools like polymarket_edges by focusing on time-series decay.

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?

Explains when to use it (differentiating old vs new edges) and what the response includes. No explicit when-not or alternatives, but sibling context implies polymarket_edges for raw edges.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: walks the order book ladder, returns verdict and forced directional risk, and explains partial basket fill consequences. 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 well-structured with clear headings (SINGLE-MARKET, BASKET) and lists all return fields. However, it is somewhat verbose and could be trimmed slightly without losing essential information. Still efficiently uses sentences.

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

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

Despite no output schema, the description thoroughly documents all return fields for both modes, including edge cases like thin_legs, max_clean_notional, forced_directional_risk, and verdict categories. It covers the dominant loss mode, making it highly complete for a complex 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%, baseline 3. The description adds meaning beyond schema: explains mutual exclusivity of market and event, side defaults, and size_usd interpretation differences between modes (spend vs target proceeds vs settlement notional). This justifies the higher score.

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

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

The description clearly defines the tool as a realizable-vs-theoretical edge check against live CLOB order-book depth. It specifies two distinct modes (single-market and basket) and lists all return fields, making the purpose precise and unambiguous.

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

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

Explicitly states when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Also warns about risks of partial fills and theoretical overround not being capturable, providing clear when-not-to-use 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?

Beyond annotations (readOnly, idempotent, non-destructive), the description details safety fields (compatibility_warning, temporal_alignment, skipped counters) and explains conditions when no arb exists. 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?

Well-structured with summary, modes, response, and safety fields. Every sentence adds value, though slightly verbose for the complexity. Could be tightened slightly 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?

No output schema, but description fully explains response (leg-by-leg prices, spreads, safety fields). Covers input, behavior, and output comprehensively.

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 description adds meaning by listing topic shortcuts, providing examples, and explaining that explicit parameters override topic mappings, going beyond the schema's basic descriptions.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket, distinguishing it from siblings like polymarket_arbitrage. It specifies two modes (topic and explicit) and the response 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?

Explicit guidance on when to use each mode: topic for pre-mapped shortcuts, explicit for custom pairings. Also warns that most pre-mapped topics return compatibility warnings, managing expectations.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. Description adds scoping detail ('Scoped to your identifier') and pairing info, but doesn't need to cover safety since annotations cover it.

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 with no waste. Front-loads the core action and adds useful context 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?

For a simple tool with one optional parameter and no output schema, the description fully covers purpose, usage, scope, and relationships. No missing information.

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

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

Schema coverage is 100% (key described as 'Memory key to retrieve (omit to list all keys)'). Description repeats this and adds listing behavior context, but baseline 3 is appropriate since schema already clarifies.

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 retrieves a value saved via remember or lists all keys when key is omitted, with examples like 'user's target ticker, an address, prior research notes'. Distinguishes from siblings by mentioning pairing with remember and forget.

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

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

Explicitly says when to use: 'look up context the agent stored earlier without re-deriving it from scratch'. Implicitly covers when not to use by mentioning omissions. No direct alternatives listed, 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 already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by explaining the mark_read parameter behavior (flagging events as read for subsequent calls) and the structure of returned events, 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 concise and well-structured: it starts with the main purpose, then details return fields, filtering, and a key behavioral nuance (mark_read), all in a few sentences 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 has no output schema, the description adequately covers the return format (source, citation_uri, raw payload) and usage context (polling, alternative HTTP endpoint). All five parameters are addressed with examples, making the tool easily usable.

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 descriptive names. The description enhances understanding by giving concrete examples (e.g., 'sec_8k' for type, ISO timestamp for since) and explaining the effect of mark_read=true, adding meaning beyond the schema definitions.

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 pulls fired events from the user's subscription feed, specifying the verb 'pull' and the resource. It lists the returned fields (source, citation_uri, raw payload), making the purpose unambiguous and distinct from sibling tools like list_subscriptions.

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 guidance by noting that polling works fine and mentions an alternative HTTP endpoint for scripts/dashboards. However, it does not explicitly contrast with sibling tools or state when not to use this tool, leaving some room for improvement.

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 behavioral context beyond these: it makes 'ONE parallel call', fans out to multiple sources, has a fallback mechanism (GDELT→GNews), and notes soft-fail for USPTO. 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 reasonably concise: it front-loads example queries, then explains behavior and parameters. Each sentence adds meaningful information. It is not overly verbose, though it could be slightly shorter 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?

The tool has no output schema, but the description adequately explains returns: 'structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs.' It also covers edge cases like fallback and soft-fail. For the complexity level, this is sufficient.

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

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

Schema description coverage is 100% (all 3 parameters documented in schema). The description adds value by providing usage tips for the `since` parameter (e.g., 'Use "30d" or "1m" for typical monitoring') and clarifying accepted formats (ISO date or relative shorthand). This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: a change feed for a company in the last N days/weeks/months via one parallel call. It provides example queries ('What's new with X', etc.) and lists specific sources (SEC EDGAR, GDELT, GNews, USPTO). It also explicitly distinguishes from sibling tool `entity_profile` by contrasting 'change feed' vs. 'static 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?

The description gives explicit when-to-use guidance through example queries and states when not to use it: 'Use entity_profile instead when you want the static profile...' This provides clear context and a direct alternative, satisfying the criterion.

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 set idempotentHint=true and destructiveHint=false. Description adds details about persistence duration and scoping by identifier, which is useful context beyond annotations.

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

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

The description is a single paragraph of 5 sentences, front-loaded with action and purpose. Could be more structured (e.g., bullet points) but remains clear and no wasted words.

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

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

Given the simplicity of the tool (2 parameters, no output schema), the description covers all needed aspects: purpose, usage, behavior, and pairing with related tools.

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 3. Description adds naming conventions and examples for values, but schema already provides adequate 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 uses specific verbs ('save', 'reuse') and resources ('data', 'key-value pair'), and distinguishes from sibling tools ('recall', 'forget') and provides examples of use cases.

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 you discover something worth carrying forward') and how to pair with recall and forget, and differentiates behavior by authentication status.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable behavioral context: it mentions cascading internal lookups and that one call replaces 2-3 manual steps, without contradicting any annotations.

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

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

The description is concise (4-5 sentences) and front-loaded with compelling examples. Every sentence adds essential information without redundancy or filler. It efficiently conveys purpose, usage, and behavior.

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 (two parameters, no output schema) and the richness of annotations and sibling tools, the description covers all necessary aspects: what the tool does, when to use it, what each parameter means, and internal behavior. No gaps are evident.

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 covers 100% of parameters, but the description significantly enriches it. For the 'type' parameter, it specifies what each enum value returns (e.g., company returns ticker, CIK, company_name, and citation URI). For 'value', it provides acceptable formats and examples, adding clarity beyond the schema's brief descriptions.

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

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

The description begins with concrete example queries ('What's the ticker for...'), immediately clarifying the tool's role. It states the purpose as resolving user-spoken names to canonical/official identifiers needed by other tools, which is a specific verb+resource. This clearly distinguishes it from sibling tools like 'entity_profile' or 'lookup' that serve different purposes.

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 includes an explicit usage directive: 'Use FIRST whenever you have a name but need an ID.' It also details the two supported entity types with specific input and output information, effectively guiding the agent on when and how to apply the tool. No alternative siblings are mentioned, but the context is strong enough to avoid misuse.

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?

Description adds significant behavioral context beyond annotations: probes each entity with ai_visibility_check, ranks by score, and returns ranked list with score, confidence, signal density. Annotations already indicate readOnly, openWorld, idempotent, non-destructive; description complements 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?

Four sentences, each earning its place: purpose, mechanism, use case example, output summary. Front-loaded with key action and differentiation. 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?

Despite missing output schema, the description specifies return format (ranked list with score, confidence, signal density). Input schema is fully covered. The entity count constraint (2-8) is in schema description. Minor omission: no mention of rate limits or error handling, but acceptable for a read-only 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 baseline is 3. The description does not add new parameter details beyond what the schema already provides; it focuses on overall workflow and output. This is adequate but does not exceed 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 it compares AI visibility across multiple entities side-by-side, probing each with ai_visibility_check, ranking by score, and surfacing most/least recognized. It distinguishes from sibling ai_visibility_check (single entity) and compare_entities (generic comparison) by focusing on competitive AI-marketing 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?

Explicitly states usage for competitive AI-marketing audits with a concrete example: "does Claude know about us as well as our competitors?". It implies when to use (comparing multiple entities) but does not explicitly exclude alternatives or mention when not to use. Still clear enough for selection.

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

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

Annotations already indicate safe, idempotent behavior. Description adds external service fan-out, partial failure degradation, and first-measurement delay (5-30s). No 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?

Single sentence packs purpose, scope, usage, limits, and error behavior efficiently. Slightly dense but not wasteful; could benefit from slight restructuring.

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

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

No output schema, but description details return fields (summary block, advisories, links, alternatives) and partial failure behavior. Fully adequate for a composite data-gathering 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 already has 100% coverage with descriptions. Description adds useful context: scoped packages accepted for 'package', default behavior for 'version'.

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's a composite check for adding npm packages, listing specific aspects (license, advisories, version history, bundle size, etc.). Differentiates from direct deps.dev usage for other ecosystems.

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 (agent asks about safety/popularity/size/cost) and when not (non-NPM ecosystems), with alternative 'deps.dev:version' mentioned.

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, idempotent, non-destructive behavior. Description adds technical details: BGE-base-en embeddings, cosine similarity over 500-char windows, 200K char cap with truncation flag. 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?

Single paragraph with efficient sentences, no wasted words. Front-loads purpose and usage, then adds technical details. Well-structured.

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

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

Even without output schema, description fully explains return type: top-N passages with character offsets and similarity scores. Also mentions truncation flag. Covers all agent needs for selecting and using the 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 covers all parameters with descriptions (100% coverage). Description adds extra context: text is 'the document text', query includes example phrases, and limit has default and range. Also explains truncation behavior for text parameter.

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

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

Description clearly states it performs semantic search inside a fetched record, gives specific examples (SEC 10-K, article), and explains output (passages with offsets and scores). It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded.

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 the record is too big to cram into the prompt') and suggests an alternative use case (pairing with ask_pipeworx_grounded). However, does not explicitly list 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?

Annotations indicate readOnlyHint=false, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral details: returns subscription id, requires OAuth, delivery webhook auto-disables after 10 failures, webhook secret returned once, SMS rate limit (10/day). 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 organized with clear headers (purpose, requirements, types, delivery). It uses bullet-like structure with line breaks. Every sentence provides value; no fluff or repetition.

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 3 parameters, no output schema, and annotations that are present, the description covers all essential aspects: purpose, prerequisites, type examples with parameters, delivery options with constraints, and return value. It is sufficiently complete for an agent to use the tool correctly.

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

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

Schema coverage is 100%, but the description adds rich semantics: examples of params per type (e.g., sec_8k with items codes, polymarket_edge with min_spread_bps), and detailed delivery options with constraints (e.g., webhook signing, email validation). This adds meaning beyond the schema.

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

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

The description clearly states the purpose: "Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id." This specifies the verb (create), resource (subscription), and distinguishes it from siblings like list_subscriptions and unsubscribe.

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 context: requires OAuth account, lists supported types with examples, and delivery channels. However, it does not explicitly state when not to use this tool or name alternative tools for similar tasks, relying on sibling names for differentiation.

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

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

The description adds significant behavioral context beyond annotations: it explains that it returns categories like 'company financials, drugs & clinical trials', and that each example includes the exact tool and argument shape drawn from a live catalog. This is consistent with readOnlyHint and idempotentHint. 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 packed with information but remains well-structured, starting with trigger phrases and explaining the output format. It is slightly long but every sentence earns its place.

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 the return structure (category-bucketed example questions with tool+argument shapes). It covers the purpose, usage, and expected results, making it complete for the tool's role.

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

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

Schema coverage is 100%, but the description adds meaning by listing specific topic values and explaining that omitting topic gives a cross-category spread. This goes beyond the schema's generic 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 identifies the tool as an onboarding entry point that returns category-bucketed example questions. It uses specific verbs like 'returns' and 'call', and the resource is 'example questions' for Pipeworx. It distinguishes itself from siblings by stating it is the first thing to use when you don't know what Pipeworx can do.

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

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

The description explicitly states when to use this tool: 'Use this FIRST when you do not yet know what Pipeworx can do for you'. It also provides guidance on optional topic filtering and mentions that it teaches how to call meta-tools, giving clear context 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?

Annotations already indicate non-destructive (destructiveHint false) and idempotent. Description adds detail: 'row is deactivated (not deleted)' and that historical events stay available, providing behavioral context beyond annotations.

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

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

Two concise sentences, front-loaded with purpose, no wasted words. Every sentence adds value: cancel action, ownership constraint, deactivation vs deletion, and historical availability.

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 single-parameter tool with no output schema, the description fully covers key aspects: action, ownership, lifecycle (deactivate), and relationship to sibling tool 'recent_alerts'. 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?

Input schema covers 'id' with 100% description coverage, stating it's a UUID returned by subscribe. Description does not add new parameter meaning beyond the schema, so baseline score of 3 applies.

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

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

Description clearly states 'Cancel a subscription by id', specifying the verb (cancel) and resource (subscription). It distinguishes from sibling tool 'subscribe' (which creates) and 'list_subscriptions' (which lists).

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 ownership enforcement: 'you can only cancel your own subscriptions.' This guides when to use. Also mentions that deactivation keeps historical events via 'recent_alerts', hinting at alternative views. No explicit when-not but strong implicit guidance.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds significant behavioral context: uses SEC EDGAR + XBRL, returns verdict with citation and percent delta, and replaces multiple sequential calls. 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 moderately long but well-structured, front-loading with common query phrases. Every sentence adds value: triggers, purpose, domain, output details, and benefits. Could be slightly more concise, but no redundancy.

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

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

Given there is no output schema, the description explains the return values (verdict, structured form, actual value with citation, percent delta). It also covers inputs, process, sources, and domain limitations, making it complete for an agent to decide whether to call this tool.

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

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

Schema description coverage is 100% for the single claim parameter. The description provides additional meaning with examples of valid claims and the expected format, which enhances understanding beyond the schema's brief 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 starts with natural-language trigger phrases and clearly states the tool does claim verification against authoritative sources. It specifies the domain (company-financial claims) and explicitly differentiates by saying it replaces 4-6 sequential calls, distinguishing it from sibling tools like ask_pipeworx_grounded.

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 states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides domain scope: 'v1 supports company-financial claims…' This implies when not to use (other claim types) but does not explicitly mention alternatives for unsupported claims, leaving a slight 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?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no additional behavioral traits (e.g., data scope, pagination, performance). Score 3 is baseline since annotations cover safety profile.

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

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

Extremely concise (one sentence) but at the expense of informativeness. Lacks structure such as separate sections for purpose, parameters, and examples; the provided schema examples are not referenced in the description.

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

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

With 5 parameters (3 required) and no parameter descriptions, the description is grossly incomplete. Although an output schema exists, the description does not help an agent understand input semantics or usage nuances.

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

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

Schema description coverage is 0%, and the description does not explain any of the 5 parameters (command, center, step_size, start_time, stop_time). The phrase 'ephemeris with table_type=VECTORS' does not clarify parameter meaning or relationships.

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

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

The description states it is a convenience function for ephemeris with table_type=VECTORS, indicating it returns ephemeris data. However, it lacks a specific verb (e.g., 'get' or 'retrieve') and does not clarify what 'convenience' implies compared to the sibling 'ephemeris' 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?

No guidance on when to use this tool versus the sibling 'ephemeris' tool or other alternatives. The description only mentions 'convenience' without explaining context or prerequisites.

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