Bundestag De

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context: the default model (Workers AI Llama-3.3-70b) is free, using `_apiKey` probes Anthropic with BYO billing, and the return structure includes per-model details and a combined view. No contradictions with annotations.

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

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

The description is concise: three sentences, each serving a purpose. First sentence defines core function, second sentence details options and returns, third sentence lists use cases. No redundant or vague phrases.

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 adequately describes return structure ('per-model {score, confidence, signals, raw_response} + a combined view'). It covers all 4 parameters, usage scenarios, and behavioral traits. The tool is well-documented 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 coverage is 100% (all 4 parameters described). The description adds meaning beyond the schema: it explains `entity` as 'Brand/business name, product name, person, or topic', clarifies the `models` array values, describes `_apiKey` purpose and format, and explains `context` for disambiguation. This adds value over 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's function: probe LLMs for knowledge about an entity and return visibility scores. It specifies the verb 'probe', the resource 'LLMs', and the output 'score (0-100) per model'. It distinguishes itself from siblings by offering cross-model visibility scoring.

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: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. It explains when to use optional parameters like `_apiKey` for Anthropic. It does not explicitly state when not to use the tool, 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 already mark this as read-only. The description adds context: it routes to multiple tools, fills arguments, and returns structured answers with citation URIs. It does not cover failure modes or latency, but the core behavior is well-described.

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-organized: starts with a strong directive, lists domains, explains internal routing, then gives usage patterns and examples. Each sentence adds value, though some redundancy exists in the examples.

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 as a router, the description covers purpose, usage, output format (structured answer with citations), and examples. No issues with missing output schema since the description indicates what is returned.

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

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

The input schema has one parameter 'question' with a minimal description. The tool's description compensates by explaining the scope of acceptable questions (e.g., 'current US unemployment rate', 'Apple's latest 10-K'), adding significant value beyond the schema.

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

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

The description clearly states the tool's purpose: routing natural language questions to over 1,400 specialized tools to answer factual queries. It explicitly distinguishes itself from web search and lists numerous domains, leaving no ambiguity.

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

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

The description explicitly states when to use this tool (prefer over web search, for factual questions about real-world data) and provides examples. It also covers the types of questions (e.g., 'what is', 'look up', 'find') and domains, giving clear guidance without needing to reference siblings.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that the tool 'pulls' data, 'fans out to packs', and 'returns an evidence packet plus comparison,' consistently describing a non-destructive, read-only operation.

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

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

The description is dense with useful information, front-loading the purpose and covering all essential aspects in a single paragraph 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?

Given the tool's complexity (multiple bet types, fan-out to packs, output format), the description covers input, process, classification, and output (evidence packet + comparison) without needing an output schema.

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

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

Schema coverage is 100% with descriptions. The description goes further by explaining the 'market' parameter accepts slug, URL, or question text, and clarifies the 'depth' parameter values and default, adding value beyond the schema.

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

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

The description clearly states it researches Polymarket bets by pulling Pipeworx data, resolving the market, classifying bet type, and returning evidence with comparison. It distinguishes itself from sibling tools, which are mostly German parliamentary or general utilities.

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 use cases like 'should I bet on X?' and notes this is the core demo product. It does not specify when not to use or name alternative tools, but given the sibling context, it's clearly the intended tool for Polymarket research.

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=true, meaning it is safe. The description adds behavioral details: what data is pulled for each type (revenue, net income, adverse events, etc.) and that it returns paired data with citation URIs, which 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 a single paragraph but is information-dense without being verbose. Every sentence contributes value, though it could be slightly restructured for readability. Overall efficient.

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

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

Given no output schema, the description explains return type (paired data, citation URIs) and covers all parameter details. It fully describes the tool's behavior and use cases, leaving no major gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the format of 'values' for company vs drug, including example tickers and drug names, which enhances understanding beyond the schema.

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

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

The description clearly states it compares 2–5 companies or drugs side by side, specifying the data pulled for each type. It distinguishes itself from sibling tools like entity_profile by focusing on side-by-side comparisons.

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 examples (e.g., 'compare X and Y', 'X vs Y') and clarifies the type parameter. While it lacks explicit when-not-to-use, the examples are clear and it mentions replacing multiple sequential calls, implying preference 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?

Description and annotations both indicate a read-only lookup (readOnlyHint=true). The description adds that it returns 'top-N most relevant tools' but omits details on ordering or pagination, which is acceptable given the annotations already cover safety.

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 coherent paragraph with clear front-loading of purpose. While efficient, it could be slightly more structured with bullet points for the list of topics, but remains appropriately sized.

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 retrieval tool with no output schema, the description covers purpose, usage guidelines, parameter details, and examples. It is sufficiently complete for an agent to understand when and how to invoke it.

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 provides full coverage (100%) with descriptions for query and limit. The description adds default value (20) and max (50) for limit, plus concrete query examples, providing extra context beyond the schema.

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

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

The description clearly states 'Find tools by describing the data or task' using a specific verb and resource, and distinguishes itself from sibling tools (e.g., entity_profile, search_activities) by focusing on discovery rather than direct access.

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 advises 'Call this FIRST when you have many tools available and want to see the option set', and provides examples of when to use (browse, search, look up) with a list of covered domains.

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, so the description does not need to restate safety. It adds value by detailing the returned data (SEC filings, revenue, patents, etc.) and mentioning citation URIs. It does not mention limitations or rate limits, but the behavior is sufficiently clear for a read-only profile tool.

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

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

The description is concise, front-loaded with the core purpose. It uses two sentences plus a list of return types. Minor improvement could be breaking the long second sentence for readability, but overall it is efficient and to the point.

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 explains the return content clearly (SEC filings, fundamentals, patents, news, LEI). It also covers input format and edge cases (names not supported). Could mention error states or data availability, but the current coverage is strong for a composite profile tool.

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

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

Schema coverage is 100%, but the description significantly enriches parameter understanding: it clarifies that only 'company' type is supported and explains that value must be a ticker or zero-padded CIK, not a name. It also provides example inputs and references an alternative tool for names.

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

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

Description uses the specific verb 'Get' and clearly identifies the resource as 'everything about a company' in one call. It lists specific data types (SEC filings, fundamentals, patents, news, LEI), distinguishing it from sibling tools like compare_entities and resolve_entity.

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

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

Description explicitly states when to use the tool: when a user asks 'tell me about X', 'give me a profile', etc. It also provides an exclusion: 'Names not supported — use resolve_entity first', guiding the agent to an alternative.

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

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

The description goes beyond the annotation (readOnlyHint=false) by specifying 'delete' and 'clear sensitive data', providing concrete behavioral context that the tool is destructive and used for memory management.

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, no redundancy, front-loaded with the core action and followed by usage guidance. Every sentence serves a purpose.

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 fully covers what the tool does, when to use it, and how it relates to siblings. 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?

With 100% schema coverage and a clear schema description for the single parameter, the description adds little extra meaning beyond 'delete by key'. The usage context is helpful but not specific to parameter details.

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

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

The description clearly states 'Delete a previously stored memory by key', providing a specific verb and resource. It also mentions pairing with 'remember' and 'recall', which helps distinguish it from sibling tools.

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

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

Explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' It also suggests pairing with related tools, guiding workflow.

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 the tool is read-only, open-world, idempotent, and non-destructive. The description adds behavioral details: it fetches the page, extracts title/description/key links, and outputs standard llms.txt markdown format. This provides context beyond annotations, though it doesn't mention auth or rate limits (reasonable given readOnlyHint). 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?

The description is concise: two sentences plus a bullet list of use cases. It is front-loaded with the core function, and every sentence adds value. No unnecessary words.

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

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

Given no output schema, the description fully explains the output ('single text blob ready to drop at site-root/llms.txt'). It covers all parameters (URL, max_links with defaults) and the process (fetch, extract, emit). Siblings are unrelated, so no confusion. The description is complete for an agent to invoke correctly.

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

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

Schema coverage is 100%, but the description adds value by specifying default values and constraints for max_links ('default 25, max 50'), which are not in the schema. The URL parameter description is effectively the same as the schema's. This additional info helps agents set appropriate values.

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

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

The description states the tool generates a production-ready llms.txt file for a given URL, specifying the verb (generate), resource (llms.txt), and context (AI crawlers like ChatGPT, Claude, Perplexity). It also lists concrete use cases, making the purpose crystal 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?

The description explicitly lists three use cases (client indexing, own project drafting, competitor auditing), providing clear guidance on when to use the tool. However, it does not mention when not to use it or suggest alternatives, but sibling tools are all unrelated, so no alternative exists for this specific purpose.

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, so the description does not need to restate that. However, it adds no further behavioral context (e.g., authentication, rate limits, or behavior if ID not found). The description is adequate but uninformative beyond the annotations.

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

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

The description is a single sentence that precisely conveys the core purpose with no filler. It is appropriately front-loaded and concise, wasting no words.

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

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

Given the simplicity of the tool (one parameter, no output schema), the description is minimally adequate. However, it omits details about the return format or what 'detail' includes, which could be helpful for an AI agent. Overall, it covers the basics but lacks completeness.

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

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

Schema coverage is 100% with a clear description of the 'id' parameter. The description does not add any additional meaning or constraints beyond what the schema provides, so it meets the baseline for this dimension.

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

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

The description clearly states the tool retrieves a Drucksache by ID, using a specific verb ('get') and resource ('Drucksache detail'). It is distinct from sibling 'search_drucksachen' which handles search.

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

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

No guidance on when to use this tool versus alternatives, such as search_drucksachen. The description only implies use when an ID is available, but lacks explicit context or exclusions.

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

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

The annotation readOnlyHint=true is already provided, but the description adds no further behavioral context. It does not mention output format, potential errors, rate limits, or any side effects. The description is too brief to enhance transparency beyond the annotation.

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

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

The description is extremely short, but this is not conciseness—it is under-specification. A good concise description would still be informative. Here, the single phrase does not earn its place as it lacks actionable content.

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

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

Given the simplicity of the tool (one parameter, no output schema), the description should clarify what details are returned. It fails to do so. The tool likely returns full protocol data, but the description does not confirm that. It is completely inadequate for an agent to understand the tool's output or behavior.

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?

Although the input schema has 100% coverage with a description for 'id', the tool description adds no additional meaning. It fails to explain the role of the parameter in context or provide format expectations (e.g., typical ID structure). The description is redundant and does not improve 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 'Plenary protocol detail.' is a vague noun phrase rather than a clear verb+resource statement. It hints at retrieving details but does not explicitly state the action (e.g., 'Get details of a plenary protocol'). The purpose is ambiguous and does not distinguish from sibling tools like search_plenarprotokolle.

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

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

The description provides no guidance on when to use this tool versus alternatives. Given the sibling search_plenarprotokolle, one would expect context that this tool retrieves a single protocol by ID, but no such indication is given.

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

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

Annotations show readOnlyHint=false, consistent with a write operation. The description discloses rate limiting, daily limits, and that feedback influences the roadmap. It also notes the team reads digests daily, providing behavioral context beyond annotations.

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

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

The description is a single, well-structured paragraph of about 100 words. It front-loads the purpose and includes every piece of information needed without redundancy. Each sentence contributes meaningfully.

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

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

Despite having 3 parameters (one nested) and no output schema, the description covers all necessary aspects: purpose, usage, formatting, limitations, and what happens to feedback. No gaps are evident.

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

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

Input schema covers all three parameters fully. The description adds value by elaborating on the 'type' enum values and giving usage tips (e.g., 'be specific', 'don't paste the end-user's prompt', '2000 chars max'). With 100% schema coverage, baseline is 3, but the additional context justifies 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 explicitly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It lists specific use cases (bug, feature/data_gap, praise) and distinguishes from sibling tools by noting it is for feedback, not querying data.

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

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

The description provides clear when-to-use guidance for each feedback type. It instructs on how to describe issues (in terms of tools/packs, not pasting prompts) and mentions rate limit (5 per identifier per day) and that it's free with no tool-call quota impact.

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

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

Annotations already declare read-only, open-world, idempotent, non-destructive. Description adds valuable context: data derived from CF analytics, no PII, cached 5min-1h. 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?

Two sentences plus bullet list. Front-loaded with purpose, then use cases, then technical detail. Every sentence earns its place with no redundancy.

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

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

For a simple read-only tool with one optional parameter, the description covers return content, data source, privacy, and caching. 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 covers 100% with enum and description. Description adds nuance: shorter windows for hot topics, longer for steady-state. This exceeds 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 clearly states it returns top tools, packs, and call volume over time windows. The purpose is distinct from siblings which are search, research, and entity tools, making it easy for an agent to select.

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 three concrete use cases (discovering hot sources, confirming canonical choices, aligning with majority use) and explains window duration trade-offs. Lacks explicit 'when not to use' but context signals indicate it's read-only, reducing need.

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, non-destructive, and open-world behavior. The description adds rich behavioral context: it walks markets, searches events, groups them, checks monotonicity, and returns ranked opportunities with reasoning. 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 coherent paragraph covering purpose, two modes with examples, and what it returns. It is slightly wordy but well-structured. 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 the complexity of two modes and cross-event search, the description covers main points but lacks details on output format (e.g., structure of ranked opportunities) and potential limitations. No output schema exists to supplement.

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

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

Schema coverage is 100% with descriptions for both parameters. The description significantly adds value by explaining the two modes and providing concrete examples for each parameter (e.g., 'Strait of Hormuz traffic returns to normal' for topic).

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: finding arbitrage opportunities via monotonicity violations on Polymarket. It details two modes (event and topic) but does not explicitly differentiate from siblings like polymarket_edges, which may overlap.

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 when to use each mode, with examples and a rationale for cross-event mode. However, it does not mention when not to use the tool or alternatives among sibling tools.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds specific behavioral details: it fetches price history once, computes model probabilities, and ranks by edge. No contradictions, and it provides useful algorithmic context.

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

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

The description is four sentences, front-loading the main purpose and method. It is efficient but could tighten some details like 'V1 covers crypto-price bets' into a separate note.

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

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

The tool has no output schema, but the description explains the return value (top N ranked by edge magnitude with suggested trade direction) and the process steps. This is sufficient for an agent to understand what to expect.

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

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

Schema description coverage is 100% with clear descriptions for all three parameters. The description redundantly mentions 'Top N edges' and 'volume window' but adds no new meaning beyond the schema.

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

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

The description clearly states it scans high-volume Polymarket markets to find where Pipeworx data disagrees with market price, and returns ranked edges with direction. It distinguishes from siblings like polymarket_arbitrage and ask_pipeworx.

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 it is built for the 'what should I bet on today' question and automates discovery without manual paging. It could be more explicit about when not to use or alternatives, 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 already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds context about the calculation method (leg-by-leg prices, spread in percentage points) and the arbitrage signal, enhancing transparency 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 informative and well-structured, front-loading the purpose. It contains several sentences but each adds value; a minor length reduction 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?

With 3 parameters, 100% schema coverage, and no output schema, the description adequately explains input modes and return values (prices and spread). It could mention error handling for unmatched events, but overall 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 coverage is 100%, but the description adds meaning by explaining the two modes and how explicit parameters override topic-mapped values. This adds value beyond the schema 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: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It distinguishes from siblings like polymarket_arbitrage by naming the specific venues and explaining the arb signal.

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

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

The description explains two explicit usage modes (topic and explicit tickers) and implies when to use this tool for arb detection. However, it does not explicitly mention when not to use it, e.g., when comparing other venues.

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=true, and description adds scope context ('Scoped to your identifier...'). No destructive traits disclosed, but with annotations providing safety profile, the extra scope detail is valuable.

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 with no wasted words. Front-loaded with core action, then usage guidelines, then pairing info. Efficient and clear.

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

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

For a simple retrieval tool with one optional param and readOnlyHint annotation, the description covers purpose, usage, scoping, and relationships to sibling tools. No missing critical context.

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

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

Schema covers the key parameter with description. Description adds the critical detail that omitting the key lists all saved keys, which is not in the schema. This adds meaning beyond schema given 100% coverage.

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

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

The description clearly states 'Retrieve a value previously saved via remember, or list all saved keys', with a specific verb and resource. It distinguishes from siblings (remember, forget) by mentioning them and their roles.

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: 'look up context the agent stored earlier... without re-deriving it from scratch'. Also explains omit key behavior. However, no explicit 'when not to use' or alternative tools beyond siblings.

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

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

Annotations (readOnlyHint=true) are consistent with description. Adds details about parallel fan-out to multiple sources and return format (structured changes + count + 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?

Front-loaded with purpose and usage examples. Two sentences cover intent and capabilities; slightly verbose but each sentence adds value. Could be tightened.

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 return values (structured changes, total_changes, citation URIs). Covers all essential aspects for agent to use 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% (baseline 3). Description adds examples for 'since' (ISO date, relative shorthand), clarifies 'value' (ticker/CIK), and notes 'type' is limited to 'company'. Significantly enhances 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 tool returns recent changes for a company, listing specific data sources (SEC EDGAR, GDELT, USPTO). Differentiates from siblings by focusing on 'what's new' vs. other entity tools.

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

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

Explicitly lists user queries that trigger the tool ('what's happening with X?', 'any updates on Y?', etc.). Implies broad monitoring via parallel sources, but lacks explicit exclusions or alternatives.

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

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

Beyond annotations (readOnlyHint=false), description adds key details: key-value scoped by identifier, persistence differences for authenticated vs anonymous users (24-hour retention), and pairing with other tools.

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-loaded with main purpose, followed by usage guidance, behavioral details, and pairing. Every sentence is necessary and well-organized.

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

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

Does not mention return value. Since no output schema exists, the description should explain what the tool returns (e.g., success confirmation or key). This omission creates a gap.

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 100% of parameters; description adds practical examples and usage context (e.g., 'a resolved ticker, a target address'), going beyond the schema's generic descriptions.

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

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

Clearly states the verb 'Save data' and the resource 'memory'. Distinguishes from siblings like recall and forget by describing the full trio.

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 advises 'Use when you discover something worth carrying forward' and contrasts with recall and forget. Does not explicitly state when not to use, but context is clear.

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

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

Annotations indicate readOnlyHint: true, and the description confirms read-only behavior. It adds transparency by stating the return type includes IDs and 'pipeworx://' citation URIs, which is valuable beyond the annotation.

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 at about 100 words, front-loaded with the core purpose, followed by usage guidance, concrete examples, and return information. Every sentence is meaningful and well-structured.

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

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

The description thoroughly covers the tool's purpose, when to use it, examples for both entity types, and what it returns (IDs, citation URIs, and additional info like ingredient and brand). It is complete given the lack of output schema.

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

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

Schema coverage is 100% with descriptions for both parameters. The description repeats examples already in the schema (e.g., 'ticker (AAPL)') but does not add new semantic meaning 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 'Look up the canonical/official identifier for a company or drug,' using a specific verb and resource. It distinguishes itself from sibling tools by specifying that it replaces multiple lookup calls.

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

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

The description explicitly says 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers,' providing clear guidance on when to use and the sequence relative to other tools.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral context: probes multiple entities, ranks by score, and surfaces most/least recognized. No contradictions, but lacks details on error handling or rate limits.

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

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

Three sentences, front-loaded with purpose, then details. Dense but efficient. Could be slightly more structured (e.g., bullet points for output), but overall 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?

Explains input, process (probes with ai_visibility_check), and output (ranked list with score, confidence, signal density). Adequate given no output schema, but could detail expected format of scores or constraints on entity count (implied by schema description of '2-8 entities').

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 meaning beyond schema: clarifies that first entity is treated as 'subject' and rest as competitors, and explains conditional use of _apiKey. Slight additional value.

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

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

Description uses specific verb 'Compare' and resource 'AI visibility across multiple entities', distinctly different from sibling 'ai_visibility_check' which handles single entities. Clearly states it ranks and surfaces most/least recognized.

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 usefulness for competitive AI-marketing audits with an example query. Implicitly relates to sibling 'ai_visibility_check' by describing how it probes each entity. Does not explicitly mention when not to use alternatives like 'compare_entities'.

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

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

The description adds significant behavioral context beyond annotations: it discloses the fan-out to two external services, the possibility of partial failures, and the first-measurement delay of 5-30 seconds for bundlephobia. Annotations already indicate readOnly, openWorld, idempotent, and non-destructive, and the description aligns perfectly 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?

The description is a single paragraph that front-loads the main purpose and then provides necessary details. It is moderately concise; every sentence serves a purpose, though it could be slightly tightened without losing 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?

Despite the lack of an output schema, the description comprehensively lists all returned fields (summary block, advisories, links, alternative versions) and covers edge cases like partial failures. Given the tool's complexity, this is fully 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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining that scoped packages are accepted and that the version defaults to latest when omitted, providing extra clarity beyond the schema.

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

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

The description clearly states the tool's purpose: a composite check for npm package safety, popularity, and size using deps.dev and bundlephobia. It explicitly mentions the specific resource (npm package) and differentiates from sibling tools by noting the NPM-only scope and alternative for other ecosystems.

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

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

The description provides explicit usage guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also specifies when not to use it by stating 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly,' and explains partial failure behavior.

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, so the description does not need to restate that. However, it adds no behavioral context beyond the brief purpose, such as pagination behavior, return format details, or any rate limits. With annotations covering the safety profile, a score of 3 is appropriate for not contradicting but also not enriching.

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

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

The description is a single, short sentence that front-loads the core purpose. While concise, it could benefit from slightly more context given the tool's complexity (8 optional parameters), but it avoids unnecessary 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?

The tool has no output schema and 8 parameters, yet the description provides no information about the return structure, what constitutes an 'activity,' or how to effectively use the parameters. This leaves significant gaps for an agent to select 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 8 parameters have descriptions in the input schema (100% coverage), so the schema already explains each one. The description adds no extra meaning or usage tips beyond the schema, meeting the baseline of 3 for high-coverage scenarios.

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

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

The description clearly states the tool provides a 'combined activity feed across Bundestag and Bundesrat,' specifying both the resource (activity feed) and scope (two chambers). Among sibling search tools, this distinguishes itself by focusing on activities rather than specific document types like search_drucksachen or search_plenarprotokolle.

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 offers no explicit guidance on when to use this tool versus alternatives (e.g., search_drucksachen for documents, search_persons for people). It implies use for a combined activity feed, but fails to mention exclusions or prerequisites, leaving the agent to infer from the name alone.

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 include 'readOnlyHint: true', which implies read-only behavior. The description only says 'Search', which is consistent, but it does not disclose any additional behavioral traits such as pagination (cursor parameter), result limits, or data freshness. With annotations already providing the safety profile, the description adds no extra transparency.

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

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

The description is a single sentence, which is short, but it omits essential details about parameters and usage. This is under-specification rather than effective conciseness.

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

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

Given the complexity of 6 parameters (including date range and cursor) and no output schema, the description fails to provide enough context for an agent to use the tool correctly. It does not explain the return format, pagination behavior, or how to combine parameters.

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 6 parameters but only 17% (drucksachentyp) have a description. The tool description does not explain any parameters; it only gives examples of document types, which loosely relates to drucksachentyp. It fails to clarify the meaning of 'num', 'query', 'cursor', 'date_from', 'date_to'.

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 'Search printed documents' with specific examples (bills, motions, answers), making the purpose easy to understand. It distinguishes from siblings like 'get_drucksache' which retrieves a single document, and 'search_plenarprotokolle' which searches plenary protocols.

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

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

No information is provided about when to use this tool versus alternatives. The description does not mention limitations, prerequisites, or contrast with other search tools in the sibling list.

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, so description need not repeat. It adds value by specifying the scope (DIP people types), but lacks details on pagination or response format.

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

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

A single, concise sentence that front-loads the purpose 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 search tool with 3 parameters and no output schema, the description lacks essential context such as pagination behavior, parameter roles (num likely page size, cursor for pagination), or result format.

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

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

Schema description coverage is only 33% (query has description). The overall description does not explain 'num' or 'cursor' parameters, failing to compensate for the low coverage.

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

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

The description clearly states the verb 'Search' and resource 'people referenced in DIP', listing specific categories (members, ministers, witnesses), distinguishing it from sibling tools like search_activities.

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

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

The description implies usage for searching people in DIP but provides no guidance on when to use this over alternatives like search_activities or search_drucksachen.

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 no behavioral context beyond the readOnlyHint annotation. It does not disclose pagination, filtering, or any side effects.

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

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

At 3 words, the description is severely under-specified. It offers no structure or useful information, failing to earn its place.

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

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

Given 5 undocumented parameters, no output schema, and multiple siblings, the description is completely inadequate. It provides no context for 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 0% and the description does not explain the meaning or usage of any of the 5 parameters (num, query, cursor, date_to, date_from). The agent cannot infer parameter semantics.

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

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

The description 'Plenary meeting transcripts.' lacks a verb, making it unclear whether this tool searches, lists, or retrieves transcripts. It does not distinguish from the sibling 'get_plenarprotokoll', which likely retrieves a single transcript.

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

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

No guidance is provided on when to use this tool versus alternatives like 'search_drucksachen' or 'get_plenarprotokoll'. There is no mention of prerequisites or exclusions.

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

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

Annotations already declare readOnlyHint=true, so the tool is safe. The description adds significant behavioral context beyond annotations: it specifies the data source (SEC EDGAR + XBRL), the limited domain (v1 supports company-financial claims), and the specific verdict types returned (confirmed, approximately_correct, etc.). It also explains the output includes citation and percent delta. No behavioral aspects are hidden.

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: three sentences covering purpose, usage, domain, and return value. It is front-loaded with the core action and uses no filler. Every sentence adds essential information, earning its place.

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

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

For a tool with one parameter and no output schema, the description is fully complete. It explains the purpose, usage context, supported domain, and return structure (verdict, structured form, actual value, citation, percent delta). The agent has all the information needed to select 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?

The input schema (100% coverage) already describes the 'claim' parameter. The description adds key semantics: it defines the type of claims supported ('company-financial claims (revenue, net income, cash position for public US companies)') and the data source. This helps the agent craft appropriate claims. The description compensates for the schema's generic description.

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

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

The description clearly states the tool's purpose: 'Fact-check, verify, validate, or confirm/refute a natural-language factual claim or statement against authoritative sources.' It specifies the resource (factual claims) and uses a specific verb. The tool is distinguished from siblings like 'ask_pipeworx' by focusing on verification and providing structured verdicts.

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

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

The description explicitly tells when to use the tool: 'Use when an agent needs to check whether something a user said is true...' and provides example trigger phrases. It also defines the domain (company-financial claims for US public companies via SEC EDGAR + XBRL), implicitly indicating when not to use it (for non-financial claims). The mention that it replaces 4-6 sequential calls adds efficiency guidance.

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