Moldova Tenders

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by detailing output structure (per-model score, confidence, signals, raw_response) and explaining the cost model (free default vs. BYO key for Anthropic). 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 core action and quickly covers defaults, optional features, return format, and use cases. Every sentence adds value with no redundancy.

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

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

Given the tool complexity (4 params, no output schema, good annotations), the description fully covers input semantics, output structure, behavioral traits, and usage context. It is sufficiently complete for an agent to understand and invoke 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?

All 4 parameters are described in the schema (100% coverage). The description goes beyond the schema by stating the default model for the 'models' parameter and clarifying that '_apiKey' is directly passed to Anthropic, which adds crucial context for correct invocation.

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 an entity and scores visibility from 0-100. It specifies the default model and optional Anthropic probing, making the purpose highly specific and distinct from sibling tools like entity_profile or 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?

The description provides clear use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') that help an agent decide when to invoke the tool. It does not explicitly exclude alternatives, but the context is sufficient.

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

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

Annotations already indicate read-only, idempotent, open-world. The description adds value by explaining it routes to 5,245 tools, fills arguments, and returns structured answers with citation URIs. No contradiction with annotations.

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

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

The description is somewhat verbose but well-structured, with key information front-loaded. Every sentence adds value, though it could be slightly more concise.

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

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

Given the tool's complexity (routing to many sources), the description covers purpose, usage, alternatives, and behavior thoroughly. No output schema is present, so return values are not needed. It is complete for agent decision-making.

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

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

Schema coverage is 100% with all parameters documented as aliases for the natural language question. The description does not add extra meaning beyond what the schema provides, only examples are given.

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

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

The description clearly states it is a routing tool for factual queries on structured data, lists specific domains (SEC, FDA, FRED, etc.), and provides examples. It distinguishes itself from sibling tools by explicitly naming alternatives 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?

The description gives explicit guidance: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and specifies when to step up to other tools for hallucination-resistant answers or broad queries. It also mentions works on every tier, providing clear context.

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, destructiveHint. Description adds beta context, candidate routing improvements, current identical behavior, and fallback to nothing (full working router). 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 moderately sized and informative, covering purpose, current state, and usage. Could be slightly more concise but is well-structured and front-loaded with key 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?

Given the tool has 6 parameters (all documented in schema), no output schema, and annotations covering safety, the description provides complete context: beta nature, identical behavior to stable, experimental edge, and fallback. 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?

Schema coverage is 100% with all parameters documented in input schema. Description adds no parameter-specific information beyond what schema already provides, 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?

Description explicitly states it's a beta version of ask_pipeworx, an identical universal router with same tools and arguments. It clearly says to use it like ask_pipeworx when wanting the newest routing. Purpose is 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?

Describes when to use (when wanting newest routing), current state (no candidate active, so matches stable), and how results are used (compared to stable router to decide merges). Distinguishes from sibling ask_pipeworx effectively.

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. The description adds significant behavioral context: the extra LLM call cost, the refusal reasons, and the extraction process. 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 and front-loaded with the core purpose. Each sentence adds value (process, return format, use cases, cost). It is slightly long but not wasteful.

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

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

Given the complexity (5,245 tools), no output schema, and rich annotations, the description provides a detailed return format including success fields (answer, evidence, etc.) and refusal reasons. It also covers performance trade-offs, making it complete for the agent to use confidently.

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% per context signals. The description states 'Your question in natural language. Accepts query, q, prompt, text, input as aliases.' This is similar to schema descriptions and doesn't add deeper semantics beyond what the schema already provides.

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

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

The description clearly states the tool is a 'Hallucination-resistant answer mode for high-stakes reads' and specifies it 'extracts the answer using ONLY what the tool result contains'. It distinguishes from sibling ask_pipeworx by noting the grounded nature and extra LLM call.

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

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

The description provides explicit when-to-use: 'whenever an answer will be quoted, cited, or acted on... must not invent facts'. It also says when not to use: 'prefer ask_pipeworx for casual lookups'. Alternatives are 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?

Extremely detailed on behavior: resolves market, classifies, fans out, returns evidence. Discusses edge cases (low-confidence matches, closed markets, wide spreads, resolution-rule risk). Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent and description adds substantial context beyond them.

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 long but well-structured and front-loaded with the core purpose. Every sentence contains useful information, though it could be slightly trimmed without losing clarity. The density of detail justifies the length.

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 fan-out categories, safety mechanisms, response shapes), the description is remarkably complete. It covers inputs, process, outputs, edge cases, and safety checks. No output schema exists, but the response shape is thoroughly described.

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 description adds significant value: explains market input types further, details include_raw effect, and describes depth options. Each parameter's meaning is enriched 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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question) and outputs (evidence packet + market-vs-model comparison). Distinguishes from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on researching a single bet rather than finding edges or arbitrage.

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

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

Explicitly lists use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' Provides examples of fan-out per category. However, it does not explicitly mention when NOT to use it or suggest alternative tools for other tasks, though siblings exist.

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, openWorld, idempotent, non-destructive. Description adds context: off-calendar fiscal year handling, sorting by primary metric, and return of paired data with 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?

Description is dense (7 lines) with front-loaded examples. Every sentence adds value, though minor redundancy could be trimmed.

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

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

Given complexity (two entity types, multiple data sources, sorting) and no output schema, description covers return structure, sorting, and key behaviors. Adequate for effective use.

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

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

Schema coverage is 100%, but description adds value by explaining data pulled per type (e.g., company: 10-K revenue, net income, cash, debt; drug: FAERS counts, FDA approvals, trials) and clarifying values (tickers/CIKs for company, names for drug).

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 does side-by-side comparison of 2-5 companies or drugs in one parallel call, with specific verbs like 'compare', 'rank', 'head to head'. It distinguishes from siblings by explicitly preferring over sequential single-pack 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?

The description provides clear when-to-use guidance ('ALWAYS PREFER over sequential single-pack lookups when comparing entities') and example queries. It does not explicitly state when not to use, but the purpose is well-defined.

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 extensive behaviors beyond annotations: parallel decomposition, gap recovery, contradictions scan, semantic excerpting, hop field, citation format (pipeworx://), and expected latency (15-90s). No contradiction with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint).

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?

Front-loads critical info (account required, sibling distinction). Despite length, every sentence adds value, with clear structure and no redundancy. Efficiently packs detailed behavioral and usage 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 tool complexity (2 params, no output schema, rich annotations), description fully explains inputs, outputs (findings packet with evidence, confidence, source, fetched_at, citations, gaps), behavior per depth, edge cases (breaking news returns gaps), and latency. Leaves no ambiguity.

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

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

Schema already covers both parameters with 100% description coverage (baseline 3). The description adds significant value by explaining the algorithmic behavior per depth value (quick=3 facets, standard=5+gap recovery, thorough=8+iterative+contradictions), which is beyond schema.

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

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

The description clearly states the tool performs 'grounded multi-source research across Pipeworx's 1369 STRUCTURED data sources' in one call, using decomposition and parallel routing. It distinguishes itself from siblings like ask_pipeworx by specifying it's for broad/multi-part questions rather than single 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?

Explicitly provides when to use (broad/multi-part questions over structured data) and when not to (single lookup or breaking/current news), with direct alternatives mentioned (ask_pipeworx). Also notes account requirements and paid tier for thorough depth.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by specifying the output format (top-N tools with full schemas and examples, ready to call) and the maximum limit (50), which are not in 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 efficient, front-loading the core purpose and then listing relevant domains. Every sentence adds value, though slight trimming could improve 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?

For a tool with 6 parameters, complete schema coverage, and no output schema, the description satisfactorily explains the rich return format (tools with schemas and examples) and usage context, making it self-contained for the agent.

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

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

Schema coverage is 100% with descriptions for each parameter, so the description's mention of aliases and default limit adds minimal new information. The baseline of 3 is appropriate as the schema already carries the semantic load.

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 discovers tools by describing a task or data domain, listing many examples. It distinguishes itself from siblings by framing it as a meta-tool to be called first when many tools are available, not for answering queries directly.

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 says 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available'. This provides strong when-to-use guidance, though it lacks explicit when-not-to-use or alternative tool names.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. Description adds detailed behavioral context: fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF; returns specific fields; notes USPTO sunset soft-fail; explains return structure. No contradiction with annotations.

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

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

Description is relatively long but front-loaded with key phrase and examples. Every sentence adds value, though slightly verbose. Structured with examples and details.

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

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

No output schema, but description thoroughly explains return fields and their sources (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI). Covers all important aspects for the agent to understand what it will get.

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. Description adds meaning: explains 'type' enum (only company), 'value' can be ticker or zero-padded CIK, names not supported, with examples. Provides context 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: 'full cross-source profile of a US public company in ONE parallel call.' It specifies the verb (return profile), resource (US public company), and distinguishes from siblings like 'resolve_entity' by noting names are not supported. The examples reinforce the purpose.

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also provides guidance: use 'resolve_entity' first if only have a name, and pass ticker or CIK. Clear when to use and when not.

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 aligns with annotations (destructiveHint=true, readOnlyHint=false) by stating 'Delete'. Adds context that it clears sensitive data the agent saved earlier. 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 efficient sentences: first states purpose, second gives usage guidance. No redundancy or irrelevant detail.

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

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

Simple tool with 1 param and no output schema; annotations cover destructive behavior. Description covers purpose and usage. Missing: error handling for missing keys, but acceptable given simplicity.

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

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

Schema coverage is 100% (single parameter 'key' described in schema). Description does not add meaning beyond the schema; it simply restates 'Memory key to delete'. Baseline 3 applies.

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

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

Description clearly states 'Delete a previously stored memory by key' – a specific verb and resource. It distinguishes from sibling tools 'remember' and 'recall'.

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

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

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. No explicit when-not, but the positive guidance is strong.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds process details (fetches page, extracts data, emits markdown) and output format, providing behavioral context beyond annotations. No contradictions with annotations.

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

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

Description is a single, well-structured paragraph with front-loaded main action and concise use case list. Every sentence adds value with no wasted words.

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

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

For a tool with no output schema, the description explains the return format and process adequately. Parameters are fully documented in schema, and use cases cover the scope. No missing context that would hinder an agent's 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 is 100% with both parameters fully described. The description does not add additional semantics beyond what the schema already provides, so baseline score 3 applies.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying verb, resource, and target. It distinguishes from sibling tools by focusing on a unique functionality not present in any listed sibling.

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

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

The description provides explicit use cases (client site indexing, personal project drafting, competitor auditing), giving clear context for when to use. It does not explicitly exclude scenarios, but the provided examples sufficiently guide appropriate usage.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds return field details and the include_inactive parameter behavior, complementing the annotations without contradiction.

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

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

Two concise sentences with no extraneous text. All information is front-loaded and relevant.

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 list tool with comprehensive annotations and schema, the description fully covers purpose, usage, return format, and parameter behavior. 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?

Schema coverage is 100% and the parameter description in the schema is clear. The tool description adds no extra meaning to the parameter beyond what the schema provides.

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

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

The description clearly states it lists the caller's active subscriptions and enumerates return fields (id, type, params, etc.). It distinguishes from sibling tools like subscribe/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?

It explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel,' providing clear when-to-use context and implying 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, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details about the specific fields returned (title, description, buyer, etc.). It does not contradict annotations and provides useful context beyond what annotations convey.

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

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

The description is two sentences long, front-loaded with the core purpose, and includes all necessary details. 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?

With no output schema, the description comprehensively lists the fields returned. For a read-only tool returning a single record, this is sufficient. It does not mention error handling or rate limits but they are not critical here.

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

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

The input schema has one parameter 'ocid' with a description. Schema coverage is 100%. The description reiterates the parameter with an example, adding minimal meaning beyond the schema. Baseline 3 is appropriate since schema already does the work.

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

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

The description clearly states the tool retrieves full details for a single Moldova tender by ocid. It distinguishes from sibling tools by specifying it is for a single tender and mentions how to obtain an ocid from moldova_recent_tenders or moldova_search_tenders.

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

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

The description tells when to use the tool: when you have an ocid and want full details. It also provides explicit guidance on where to get an ocid from other tools. It does not explicitly state when not to use it, but the context is clear.

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

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

Annotations (readOnlyHint, idempotentHint) already indicate safe read-only operation. Description adds context: data as OCDS, Romanian titles, and what fields are returned. No destructive behavior.

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

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

Three concise sentences covering purpose, output details, and sibling tool. No redundancy, 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 tool with one parameter and no output schema, the description is complete: source, format, fields, language, and related tool. 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?

Schema description coverage is 100%, so baseline is 3. Description does not add new meaning beyond schema's explanation of 'limit' parameter (range 1-50, default 20).

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 recent government procurement tenders from Moldova's MTender platform, lists exact fields (ocid, title, buyer, value, status, etc.), and distinguishes from sibling moldova_get_tender for full detail.

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 (get recent tenders) and provides alternative for full detail (moldova_get_tender). Could mention moldova_search_tenders for queries, but current guidance is sufficient.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it is a client-side filter (no native full-text search in MTender), case-insensitive, handles Romanian text, and explains the scan parameter trade-off. 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?

Two sentences: first states purpose with resource and action, second explains limitations and return shape. No extraneous information, front-loaded with key details. Every sentence adds value.

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

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

For a simple search tool with full schema and annotations, the description covers purpose, behavior (client-side filter), limitations (only recent), and parameter trade-offs. It references the return shape via another tool, which may require that tool's description for full clarity. Lacks error handling or empty result behavior, but is otherwise complete.

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

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

Schema description coverage is 100%, with each parameter described. The description adds extra semantics: scan parameter explains that higher values are more thorough but slower, and query describes case-insensitive matching against title/buyer/CPV description. This goes beyond the schema's basic descriptions, justifying a 4.

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 performs a keyword search over recent Moldova MTender procurement tenders. It specifies the resource (tenders), action (search), and scope (recent, Moldova). It implicitly distinguishes from siblings like moldova_recent_tenders (which lists without search) and moldova_get_tender (single tender retrieval).

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

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

The description explains that it is a client-side filter over recent tenders and matches only recent listings, implying a limitation. However, it does not explicitly state when to use this tool versus alternatives like moldova_recent_tenders or when not to use it. It only notes the return shape resembles moldova_recent_tenders, lacking clear separation of use cases.

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

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

Annotations are all false, and the description adds critical behavioral details: rate-limited to 5 per identifier per day, free, and doesn't count against tool-call quota. 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-organized: starts with purpose, then usage scenarios, then constraints. Every sentence serves a purpose 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 3 parameters with full schema coverage, no output schema needed. The description covers usage, constraints, and context, making it fully complete for an AI agent.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying usage for each type (e.g., 'bug = something broke') and instructing not to paste end-user prompts, which is not in schema.

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

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

The description clearly states the tool's purpose: sending feedback about bugs, feature requests, data gaps, or praise. It distinguishes the tool from siblings by focusing on communication with the Pipeworx team, unlike research or query tools.

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

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

The description explicitly lists when to use the tool (for bug, feature, data_gap, praise) and what not to do (don't paste end-user prompts). It also notes rate limits and quota exemption, providing comprehensive 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 the readOnlyHint, openWorldHint, and idempotentHint annotations, the description discloses caching behavior (5min-1h depending on window), data source (CF analytics-engine), and that no PII is involved. This adds valuable context about the tool's behavior.

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

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

The description is concise with two paragraphs: first states core purpose, then lists use cases and technical notes. Every sentence adds value with no redundancy or fluff.

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

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

For a tool with no output schema, the description explains returns (top tools, top packs, total call volume) and caching. While the exact structure of 'top tools' is not specified, it's sufficient for an agent to infer the output. Given the simplicity, it is complete enough.

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 single parameter 'window' is fully covered by the schema description with enum values and explanation. The tool description adds that shorter windows surface what's hot now and longer show steady-state demand, enhancing understanding beyond the schema.

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

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

The description states 'What other AI agents are calling on Pipeworx right now' and specifies it returns top tools, packs, and call volume over a window. This provides a specific verb (returns) and resource (trending data from Pipeworx), clearly distinguishing it from sibling tools like ask_pipeworx or discover_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?

Three explicit use cases are listed: discovering hot data sources, confirming canonical tool, and seeing if use case aligns. It also clarifies that the signal is self-aggregating, no PII, and cached, providing context on when and why to use this tool over 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 mark it as read-only, idempotent, not destructive. Description adds deep behavioral detail: scanning process, Jaccard similarity filter, partition placeholder filter, and the fill check against live CLOB depth. 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 clear sections (introduction, modes, filters, response, fill check). Every sentence adds valuable information; no fluff. Front-loaded with core action.

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

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

Despite no output schema, the description fully explains the response structure (opportunities[], partition_check{}, fill check) and covers edge cases (placeholders, low similarity). It is complete for a complex analysis 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 both parameters with descriptions. The description adds examples of valid values, explains the mode differences, and recommends event mode, providing 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 identifies the tool as finding arbitrage opportunities on Polymarket using specific methods (monotonicity violations, partition-sum checks). It distinguishes two modes (event and topic) and mentions related signals like fill check, differentiating it from sibling tools like polymarket_edges or 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?

Explicit guidance: event mode recommended for a specific market with examples; topic mode for cross-event scanning. Also warns when not to trade (realizable_edge_pp ≤ 0) and points to polymarket_fill_risk for custom sizing.

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, open-world, idempotent, non-destructive behavior. The description goes far beyond: it explains caching (1h at KV level), detailed model families and edge calculations, tradeable-edge knobs, response structure including diagnostics, and warning conditions like 24h-move warning. 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?

Though lengthy, the description is well-structured with clear sections (segments, knobs, response fields, diagnostics). Every sentence provides essential detail—no fluff. Front-loaded with purpose and audience. The density is appropriate for the complexity of the tool.

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 9 parameters, no output schema, and high complexity, the description fully compensates. It details response structure (by_segment, diagnostics, fed_candidates), explains what each segment contains and why, and clarifies handling of edge cases like partition kelly. No gaps detected.

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 all 9 parameters with descriptions (100% coverage). The tool description adds context by grouping parameters (knobs, filters) and explaining interactions like partition_overround kelly behavior vs. single-leg. This adds meaning beyond the schema, justifying a score above baseline.

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

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

Description begins with a clear action verb-resource pair: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It immediately distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edge_tracker by focusing on Pipeworx data alignment and opportunity discovery without manual paging.

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

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

The description explicitly states the intended use case: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It implicitly differentiates from siblings, but does not explicitly mention when not to use this tool or list alternatives for edge tracking vs. arbitrage vs. fill risk.

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

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

Annotations declare readOnlyHint and idempotentHint true. The description confirms read-only behavior and adds extensive behavioral details: source from daily snapshots, computed fields (trend, decay), data gaps due to cache-miss, and TTL boundaries. 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 detailed but well-structured: opening statement, response structure (tracked, expired, snapshot_dates), and limits. It is efficient without fluff, though a bit dense. Every sentence adds value.

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

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

No output schema exists, so the description must fully explain return values. It does so comprehensively, detailing tracked (time-series, first_seen, trend, decay_pp_per_day), expired (lifespan_days), snapshot_dates, and limitations. 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 covers both parameters (days, window) with defaults and ranges. The description adds context: days as lookback with clamp 2-30, window as snapshot family. With 100% schema coverage, baseline is 3; the description provides extra meaning, justifying a 4.

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

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

The description clearly states it provides edge persistence and decay telemetry from daily snapshots, answering a specific question about edge duration. It distinguishes itself from sibling tools like polymarket_edges by focusing on historical tracking rather than current snapshots.

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 when to use: when needing to know how long an edge has existed and if it's shrinking. It notes limits (60-day TTL, snapshot gaps) but does not explicitly state when not to use alternatives. However, the context is clear enough for an agent.

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 aligns with annotations (readOnlyHint, idempotentHint, destructiveHint=false) and adds behavioral details beyond them: it describes walking the order book ladder, returning verdicts like 'clean|degraded|cannot_fill', and identifying forced directional 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?

The description is informative and well-structured, front-loading the purpose and then detailing modes. While slightly verbose with technical details, every sentence adds value. Could be slightly more concise.

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

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

Despite no output schema, the description fully enumerates all return fields for both modes (e.g., top_of_book, vwap_fill_price, slippage_pp, verdict for single-market; theoretical_sum, realizable_sum, thin_legs[], forced_directional_risk for basket). Complete for a read-only check 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%, but the description adds value by explaining the mutual exclusivity of `market` and `event`, default behaviors for `side` and `size_usd`, and interpretation of `size_usd` in basket mode. Slightly above baseline.

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

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

The description clearly states the tool's purpose as a 'realizable-vs-theoretical edge check against live CLOB order-book depth'. It distinguishes between single-market mode and basket mode, which helps differentiate it from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

The description explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It explains the risks of thin books and partial fills, providing clear when-to-use and 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?

The description adds significant behavioral context beyond the annotations: it details safety fields (compatibility_warning, temporal_alignment, skipped counters), response structure, and caveats about event equivalence. It does not contradict annotations (readOnlyHint, idempotentHint, destructiveHint=false).

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 thorough and well-structured with sections, but somewhat lengthy. However, every sentence contributes value, and it is front-loaded with the core purpose. Minor deduction for verbosity, but it remains efficient.

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

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

Despite no output schema, the description fully explains the return format (leg-by-leg prices, matched spreads, safety fields) and how to interpret them. It covers all necessary context for selecting and invoking 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 meaning by explaining the topic shortcuts list, providing examples, and describing the override behavior between topic and explicit parameters. This enriches the schema's 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 the cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes from sibling tools like polymarket_arbitrage by specifying it's a cross-venue comparison, and provides two explicit modes (topic and explicit).

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 and when-not-to-use guidance, including that most pre-mapped topics return compatibility warnings and are not tradeable. It explains the two modes and when to use explicit over topic, and clarifies interpretation of safety fields.

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 and idempotentHint; description adds scoping details (anonymous IP, BYO key hash, account ID) and pairing behavior, 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?

Three sentences, front-loaded with main function, no wasted words. Each sentence provides unique value.

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

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

Covers purpose, usage, and scoping well for a simple read tool. Minor gap: no explicit description of return format (value vs list), but it is implied.

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

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

Schema coverage is 100% and already explains the key parameter and omission behavior. Description adds only minor context (previously saved) 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 uses specific verbs (retrieve/list) and resource (memory saved via remember), and distinguishes from siblings (remember, forget) by mentioning pairing and scoping.

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 (to look up stored context, avoid re-deriving), and pairs with remember/forget for different operations. Also explains scoping.

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 idempotent, read-only, non-destructive. The description adds behavioral details: mark_read changes state (flags events as read), which is a side effect beyond annotations. It also mentions the feed persists, which is useful context. 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 front-loaded with the main purpose, then details parameters and usage. It is informative but slightly lengthy (6 sentences). Could be more concise, but every sentence adds useful 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 complexity (5 optional params, no output schema), the description covers primary usage, filtering, state modification (mark_read), and alternative access. Lacks error handling or permission details, but sufficient for a read-oriented tool with good annotations.

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?

All 5 parameters are described in the schema (100% coverage). The description adds value beyond schema with examples like 'sec_8k' for type, explains mark_read's effect, and clarifies ISO timestamp format for since. Default for limit is mentioned (50). This exceeds baseline 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 pulls fired events from the subscription feed, specifying the resource (alerts), verb (pull/returns), and key attributes (source, citation_uri, raw payload). It distinguishes itself from siblings like 'list_subscriptions' by focusing on alert events rather than 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 clear context for when to use the tool (polling is fine, alternative endpoint for scripts) but does not explicitly contrast with sibling tools or state when not to use it. The guidance is adequate but lacks explicit exclusion criteria.

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. Description adds rich behavioral context: fan-out to SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail, return structure with changes grouped by source and citation URIs. No contradiction with annotations.

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

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

Description is somewhat long but front-loaded with example queries and packs essential information efficiently. Every part earns its place, though minor redundancy (e.g., repeating 'when you want the static profile') could be trimmed.

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

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

Despite no output schema, description explains the return structure (changes grouped by source, total_changes count, citation URIs). It covers data sources, parameters, usage, and limitations (USPTO soft-fail). For a complex multi-source tool, this is thorough.

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 description adds value: for 'since' it explains ISO vs relative formats and recommends '30d' or '1m'; for 'value' it gives examples of ticker and CIK; for 'type' it notes only 'company' supported. This enhances understanding beyond the schema alone.

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

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

Description opens with concrete example queries ('What's new with X', 'latest on Y') and states it provides a change feed for a company over a time window using multiple data sources. It explicitly distinguishes itself from sibling tool 'entity_profile', which is for static profile. The purpose is specific and clear.

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 provides clear examples of when to use (news/updates queries) and explicitly says when not to ('Use entity_profile instead when you want the static profile'). It explains the parallel data sources and fallback behavior, guiding the agent's decision.

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 behavioral traits beyond annotations: key-value scoping by identifier, persistence differences between authenticated users (permanent) and anonymous sessions (24 hours). Annotations already mark idempotentHint=true; description adds lifetime context 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 and well-structured: opens with purpose, then usage guidance, then storage details. Every sentence adds value without redundancy. Three sentences, no filler.

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

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

Given the tool's simplicity (two required parameters, no output schema), the description fully covers behavior, scoping, and lifetime. No gaps remain.

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

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

Schema has 100% coverage with clear descriptions for key and value. The description adds examples (e.g., 'subject_property') but does not provide additional meaning beyond the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later.' It specifies the key-value pair format and concrete examples (ticker, address, preference), distinguishing it from sibling tools recall and forget.

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

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

Provides explicit guidance on when to use: 'when you discover something worth carrying forward.' Mentions pairing with recall and forget, but does not explicitly state when not to use it or alternatives for non-persistent data.

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 operation. Description adds valuable context: internal cascading through multiple endpoints, replacing 2-3 manual lookups. 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 concise, front-loaded with examples and clear use. 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?

For a tool with 2 required params and no output schema, the description fully covers purpose, usage, return values, and entity types. It explains both supported types and what they return, making it complete.

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

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

Schema coverage is 100%, but description adds meaning beyond schema: explains auto-disambiguation for company name, lists return fields (ticker, CIK, RxCUI, etc.), and gives example inputs. This significantly aids correct parameter usage.

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

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

The description clearly states the tool resolves names to canonical identifiers, uses specific verbs and examples, and distinguishes from siblings by emphasizing 'use FIRST when you have a name but need an ID'.

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

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

Provides explicit when-to-use guidance ('use FIRST whenever you have a name but need an ID') and clear context. Does not explicitly list when not to use, but given sibling tools, context is sufficient.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as safe. The description adds behavioral details: it internally probes each entity with ai_visibility_check, ranks by score, and returns score, confidence, and signal density per entity. 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 three sentences long, front-loaded with the core purpose, then explains the mechanism and output. Every sentence adds value without redundancy or unnecessary detail.

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

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

Despite lacking an output schema, the description specifies the return format (ranked list with score, confidence, signal density per entity). It notes the entity limit (2-8) and mentions optional parameters. The annotations cover safety and behavioral hints, so the description is sufficiently complete for its complexity.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by specifying that the first entity is treated as the 'subject' for narrative and that 'context' is applied globally. This extra meaning about entity ordering and shared context elevates the score above baseline.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, probing with ai_visibility_check and ranking results. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison) by emphasizing competitive AI-marketing audits with a subject vs. competitors framing.

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 a concrete use case ('competitive AI-marketing audits') and an example question. However, it lacks explicit guidance on when not to use this tool versus alternatives like ai_visibility_check for single entities or compare_entities for other comparisons, though the context implies the distinction.

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 annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia may time out on first measure (5-30s), and 'sources_failed' lists any failed source. This goes 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 dense but well-structured: one paragraph front-loading the purpose, then usage guidance, ecosystem scope, behavioral notes, and output summary. Every sentence adds value, and it is concise 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?

Despite no output schema, the description fully explains the return format (summary block, per-advisory detail, links, alternative versions) and edge cases (partial failures, timing). For a composite tool, it is remarkably complete.

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

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

Schema coverage is 100% with descriptions. The description adds important context: scoped packages (@types/node) are accepted, and version defaults to latest when omitted. This enhances usability beyond the raw schema.

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

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

The description clearly states the tool's composite purpose: checking whether to add an npm package by combining deps.dev and bundlephobia analyses. It distinguishes from siblings by specifying the ecosystem (npm only) and the unique aggregation of license, advisories, version history, and bundle metrics.

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 usage context is provided: 'Use whenever an agent asks "is X safe / popular / small"...'. It also clarifies limitations ('NPM ecosystem only in v1') and directs other ecosystems to a sibling tool, which is good implicit guidance. However, it does not explicitly state when to avoid this 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 readOnlyHint, openWorldHint, idempotentHint. Description adds significant value: embedding model (BGE-base-en), chunking (500-char overlapping windows), character offsets, similarity scores, and 200K char cap with truncation flagging. 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?

Description is four sentences, each serving a clear purpose: core functionality, usage scenario, pairing, and technical details. No redundant information, front-loaded with the most critical insight.

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 is provided, but the description mentions return format (passages with character offsets and similarity scores). It covers input limits, truncation behavior, and embedding details. Slight lack of explicit passage field names, but otherwise complete for agent usage.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds marginal detail (max chars for 'text', query examples, limit range), but does not fundamentally extend what the schema provides. 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 'Semantic search INSIDE a fetched record' with specific verb and resource. It distinguishes itself from siblings by mentioning pairing with 'ask_pipeworx_grounded' and contrasting with 'fetch with the gateway'.

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 when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter.' Also mentions pairing with another tool. Lacks explicit when-not-to-use, but context is clear.

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

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

The description discloses critical behavioral details beyond annotations: requires OAuth account, details on each subscription type with parameters, delivery channels with constraints (e.g., SMS phone verification and 10/day cap), and webhook signing information. 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 and front-loaded with the core action. Every sentence provides necessary information without redundancy. It efficiently conveys requirements, types, and delivery options.

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 description covers the main functionality, requirements, supported types with examples, and delivery details. It lacks explicit mention of error handling or return value details beyond the subscription ID, but given the complexity and absence of output schema, it is sufficiently complete.

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

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

Schema coverage is 100% with descriptions for each parameter, but the tool description adds significant value by explaining the meaning of each subscription type, examples, and delivery channel specifics (e.g., always-on feed, SMS verification, webhook HMAC). This goes beyond the schema alone.

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

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

The description clearly states the tool creates a proactive monitoring subscription and returns a subscription ID. It distinguishes itself from sibling tools like list_subscriptions, unsubscribe, and recent_alerts by focusing on creation.

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

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

The description specifies that an OAuth account is required and that anonymous/BYO cannot persist subscriptions, providing a clear condition for use. It also details supported types and delivery channels, but does not explicitly mention when not to use it or suggest alternatives beyond the context.

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, and destructiveHint. The description adds valuable behavioral context: it returns example questions drawn from a live catalog, can be focused by topic, and is non-destructive. 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 relatively long but well-structured with alternative phrasings, purpose, return value details, and usage guidance. Every sentence adds value, though it could be slightly trimmed 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 no output schema, the description thoroughly explains what the tool returns (category-bucketed examples with tool shapes) and how to use it. It covers all necessary information for an onboarding tool with one optional parameter.

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?

Only one optional parameter (topic) with full schema description. The description adds meaning by listing possible values (e.g., 'finance', 'pharma') and explaining the effect of omitting vs. specifying, beyond what the schema provides.

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

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

The description clearly states the tool's purpose as an onboarding entry point that returns category-bucketed example questions with exact tool and argument shape. It distinguishes itself from sibling tools by being the first tool to use when learning capabilities.

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

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

Explicitly says to use this FIRST when you don't know what Pipeworx can do, and to learn how to call meta-tools. Provides context for when to pass the topic parameter, but does not explicitly state when not to use 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?

Discloses ownership enforcement, deactivation rather than deletion, and impact on historical events, going beyond annotations which only indicate non-destructive and idempotent.

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 action, every sentence adds value.

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

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

For a simple one-parameter tool with annotations, the description fully explains behavior and constraints, leaving no ambiguity.

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

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

Schema already fully describes the 'id' parameter (100% coverage). Description does not add new meaning but is consistent.

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

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

Description clearly states the action ('Cancel a subscription by id') and distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by specifying ownership enforcement and deactivation behavior.

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 (cancel subscription by id) and ownership constraint, but does not explicitly mention alternatives or 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 already indicate read-only, idempotent, non-destructive. Description adds detail: it uses a fast path for financial claims and a grounded pipeline for others, returns verdict with evidence, and replaces multiple 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?

Description is front-loaded with natural language triggers and is structured logically. While verbose, most sentences earn their place with essential information. Could be slightly more concise, but still effective.

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

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

Given the tool's complexity (2 params, 100% coverage, annotations present, no output schema), the description fully covers purpose, usage, behavior, parameters, and output. It enables an agent to use the tool correctly without ambiguity.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining the tolerance_pct default and use case for hallucination detection, and provides examples for the claim parameter, 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?

The description uses a specific verb ('verify') and resource ('natural-language claim'), and lists example triggers. It distinguishes from siblings by noting it replaces multiple sequential calls and handles both financial and non-financial claims.

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 'Use whenever the agent needs to check whether something a user said is factually correct.' Also clarifies when to use for company-financial claims vs other claims, providing clear guidance on alternatives.

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