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

With no annotations, the description correctly indicates a destructive action ('purge') and specifies the cache targets. However, it lacks details on rate limits, auth requirements, or side effects of clearing different caches.

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, both relevant. No fluff. Slightly more structured formatting (e.g., bullet points) could improve, but it's already efficient.

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

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

Given the tool's simplicity (1 param, no output schema), the description adequately covers purpose and typical scenario. Could mention that clearing cache is immediate or note persistence, but not necessary.

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 'target' is fully described in the schema with enum and default. The description adds no additional 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?

Clearly states the tool purges search and/or fetch result cache, distinguishing it from sibling tools that perform searches, fetches, or crawls. The verb 'purge' and specific resources are 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?

Provides a specific use case: 'researching fast-moving topics where cached results from the past hour may be stale.' Does not explicitly mention when not to use, 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?

No annotations provided; description carries full burden. Discloses caching behavior and crawl strategy (Firecrawl, sitemap-first, BFS). Could mention rate limits or error handling, but sufficient.

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

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

Two sentences, front-loaded with core purpose, each sentence adds value. No redundant information.

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

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

Covers caching, strategy, and relationship to fetch_url. Lacks detailed return structure of manifest, but adequate for a tool with no output schema and good parameter descriptions.

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 no extra meaning beyond parameter names and default values 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?

Description clearly states 'Crawl a site and return a manifest of pages' (specific verb+resource+output). Distinguishes from sibling fetch_url by noting it caches full page content for later 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?

Implied guidance: use fetch_url after crawling for full text. No explicit when-to-use or when-not-to-use compared to search or other siblings. Lacks 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 description explicitly labels the operation as read-only and states that aggregate mode reports whether the internal scan cap was hit. It does not describe error behavior or output formatting, but the read-only nature and data scope are well conveyed.

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 focused and information-dense, listing the relevant statistics and parameter behavior without unnecessary filler. The repeated read-only statement is minor but not harmful, and the overall structure is easy to scan.

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 single optional parameter and the presence of an output schema, the description adequately covers what the tool does, what data it returns, and how to select between per-domain and aggregate modes. No critical usage context appears to be missing.

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 only parameter, hostname, is fully described in both the schema and the description, including an example value and the behavior when omitted. The optional/aggregate distinction is explicit, leaving no ambiguity about 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 reads the searxng-mcp domain capability database and enumerates the specific statistical data it exposes. It is easily distinguished from the search/fetch sibling tools because its focus is domain capability statistics rather than retrieving or searching content.

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 how to use the tool: provide a hostname for one domain or omit it for an aggregate. It does not explicitly contrast when to use this tool versus the sibling search/fetch tools, but the domain-stats purpose makes the appropriate context reasonably clear.

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

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

With no annotations, the description carries full burden for behavioral disclosure. It details the fetch cascade, token budget, caching (24 hours), and refusal of blocked/internal domains. It does not mention rate limits or error handling, but the main behaviors are well covered.

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 primary action. The second sentence provides essential details without unnecessary words. Every sentence earns its place.

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

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

Given no output schema, the description explains return is clean markdown and trimmed to token budget. It covers caching and refusal behavior. It could mention return format more explicitly but is adequate for a fetch tool with moderate 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%, so baseline is 3. The description adds value by explaining the default token budget (2000 tokens / 8000 chars) and how max_tokens relates to characters. It also clarifies behavior for domain_profile, target_selector, and wait_for_selector, which goes 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 fetches and extracts readable content from any URL, with specific handling for GitHub URLs and a cascade of backends. It distinguishes itself from siblings like crawl_site (multi-page) and search (search) by focusing on single URL fetch and extraction.

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 what the tool does and provides context for when to use it (fetching any URL). It mentions blocked domains and private addresses are refused, giving a clear exclusion criterion. However, it does not explicitly contrast with siblings like crawl_site or search_and_fetch, which could be used for similar tasks.

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

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

With no annotations available, the description carries the full disclosure burden and exceeds it: it explains the reranking pipeline, the surfacing of direct answers and infoboxes in structuredContent, the 1-hour cache, and domain blocking/boosting behavior. These operational details give an agent accurate expectations beyond the basic act of searching.

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 compact: the core operation is front-loaded, and each subsequent sentence contributes a distinct behavioral fact such as structured content, caching, filtering, or tool preference. There is no filler or 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 10-parameter tool, the description plus the detailed input schema and output schema cover the important behavioral, filtering, caching, and result-format semantics. The description is complete enough for an agent to invoke the tool correctly and understand what will happen.

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

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

Schema description coverage is 100%, so the baseline is 3 even though the main description adds little parameter-specific semantics. The description does not need to repeat parameter meaning because the input schema already documents each parameter clearly; the narrative adds only behavioral context.

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 first sentence identifies a specific verb, resource, and pipeline: searching the web via the local SearXNG instance with reranking. This is clear and specific, but it does not explicitly differentiate from sibling tools such as search_and_fetch or search_and_summarize; the only alternative named is the built-in WebSearch tool.

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

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

The description explicitly says to prefer this tool over the built-in WebSearch tool, which is a useful direct comparison. It also notes that the expand option is most useful for research queries. However, it does not provide when-not-to-use guidance for sibling search-related tools, so the guidance is good but not exhaustive.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure — and it delivers substantially. It reveals the multi-step pipeline (search → rerank → fetch), the GitHub-API special path, the fallback cascade order (Firecrawl → Crawl4AI → raw HTTP), caching of results and pages, blocked-domain filtering, and the return shape (result list + clean markdown). This far exceeds what the name or schema alone would 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?

Four sentences with the core purpose front-loaded in sentence one, followed by the most operationally important details in priority order: GitHub special-casing, fetch cascade, caching/filtering, and return format. Every sentence earns its place; there is zero redundancy or filler.

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

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

For a complex 10-parameter tool with no annotations and no output schema, this is close to complete: it covers the pipeline, fetch strategy, caching, filtering, and return format. The gaps are minor — no explicit error behavior, rate limits, or detailed result-entry structure — but the highly descriptive schema compensates for most of what the description omits.

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%, and the schema itself is unusually rich — e.g., `min_score` documents bimodal score distribution, the 0.01-0.1 useful range, model-dependence, and no-op behavior. The description adds pipeline-level framing that helps connect parameters to stages (rerank → min_score, top results → fetch_count), but provides no per-parameter details beyond the schema. The baseline 3 applies as schema does the heavy lifting.

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 a specific verb chain and resource: 'Search the web, rerank results, then fetch the full content of the top result(s).' This clearly differentiates it from sibling tools `search` (search only) and `fetch_url` (fetch only) by describing the combined pipeline. The GitHub-API special case and fetch cascade further pin down exactly what the tool does.

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 pipeline description makes the usage context clear: this tool is for when an agent needs both ranked search results AND full page content. The details about caching, blocked-domain filtering, and the fetch cascade give a concrete picture of behavior. However, it never explicitly names alternatives or states when NOT to use it (e.g., 'if only URLs are needed, use search'), so exclusion guidance is left to inference.

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

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

With no annotations, the description carries the full burden and does so thoroughly: it discloses reranking, fetching limits, local LLM synthesis, the explicit fallback marker, min_score no-op warnings, and per-parameter default behaviors.

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?

Although long, the description is information-dense and every sentence adds operational detail. The fallback marker is slightly repetitive but still useful for programmatic detection.

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

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

Given the tool's complexity, the description covers search expansion, engine restrictions, categories, language, time range, fetch count, scoring thresholds, profiles, and fallback behavior. It is sufficiently complete without 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?

All 10 parameters are documented with meaningful detail beyond the schema. Especially strong for min_score, explaining the bimodal distribution, non-comparability across rerankers, and the no-op warning when unavailable.

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 a multi-step purpose: search, rerank, fetch top results, and synthesize a cited summary. Explicitly distinguishes itself from raw-page tools with 'rather than raw pages' 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?

Tells when to use it ('Best for deep research where you want pre-digested synthesis') and contrastively implies when not to use it (raw pages). Also documents fallback behavior, which helps manage expectations.

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