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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions returning go/no-go alerts and the conditions that trigger them, but does not disclose whether the operation is read-only, if it requires prior state (e.g., watched hackathons), how alerts are delivered, or any other side effects. This is minimal detail for a monitoring 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 two concise sentences with no fluff. It front-loads the action ('Check watched hackathons') and then states the output type and trigger conditions. Every word 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 the tool's role in a larger workflow (with siblings like watch_hackathon, refresh_gallery_state, diff_competitor_landscape), the description is under-specified. It does not explain the relationship to these tools, the meaning of project_id, or the expected output structure beyond 'alerts'. An output schema exists but is not shown, and the description does not need to repeat it, yet other contextual gaps remain.

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

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

The schema has one parameter, project_id, with no description (0% coverage). The description does not explicitly explain that project_id identifies the project of interest, although the phrase 'affect a project' hints at it. This is insufficient for an agent to confidently know how to set the parameter.

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

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

The description clearly states the tool checks watched hackathons for landscape changes affecting a project and returns go/no-go alerts. This specific verb+resource combination distinguishes it from siblings like diff_competitor_landscape, which likely provides diffs rather than actionable alerts.

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 context: it should be used when you have watched hackathons and want alerts about crowding or gallery shifts. However, it does not explicitly state when to use this over alternatives like diff_competitor_landscape or refresh_gallery_state, nor does it mention any prerequisites such as having called watch_hackathon first.

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

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

With no annotations provided, the description carries the full burden. It does disclose a key behavioral trait—'store a ProjectProfile'—and the return of project_id, which is useful. Yet it omits details about required inputs, error behavior, authorization needs, or what happens if multiple sources conflict, leaving the agent with an incomplete picture.

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 with no fluff. The first sentence states what the tool does and the input sources; the second details the output and downstream use. Every clause adds information, making it highly concise 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 covers the basic workflow: input sources, storage, and returned project_id for downstream tools. However, it lacks important contextual details such as input constraints, failure modes, and side-effect scope. The presence of an output schema helps, but the description alone is not fully complete for an agent to invoke the tool confidently.

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

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

Schema description coverage is 0%, so the description must compensate. It names the four input sources in natural language ('repo/demo/pitch/README'), which maps to repo_url, demo_url, pitch_text, and readme_text. However, it does not clarify whether at least one source is required, how multiple sources are combined, or expected formats, so the compensation is only partial.

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

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

The description clearly states the action ('Analyze a project') and the resource type ('from repo/demo/pitch/README') plus the output ('store a ProjectProfile'). It is specific and matches the input schema, but it does not differentiate from the sibling tool 'derive_project_profile', which likely has a similar purpose.

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

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

The description implies usage when you have a repo, demo, pitch, or README to analyze, and it notes the project_id is useful for 'scoring and competitor tools'. However, it provides no explicit guidance on when to choose this tool over 'derive_project_profile' or other alternatives, and no 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?

With no annotations provided, the description carries full burden and does well: it explains the internal steps (finds, mines, maps, returns) and explicitly frames the output as inspiration/strategy rather than copyable code or brands. It does not state whether any data is modified or if prerequisites like an existing project profile are needed, but the read-only analytical nature is clear from the wording.

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

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

The description is three tight paragraphs: a one-sentence summary, a process/output breakdown, and a usage caution. Every sentence earns its place, and the purpose is front-loaded in the first line. There is no fluff or repetition.

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

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

Given the tool's complexity and the presence of an output schema, the description covers the process and output well and the output schema handles return values. However, it omits parameter semantics and any mention of prerequisites or edge cases (e.g., what if no related ended hackathons are found). It is adequate for understanding the tool but not fully complete for correct invocation.

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

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

The input schema has 0% description coverage and the tool description does not name any of the four parameters directly. Context implies project_id is the current project and include_similar relates to 'similar hackathon winners,' but top_k_events is completely unexplained and even the required hackathon_id is only vaguely inferred. The description fails to compensate for the bare schema.

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

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

The description opens with a specific verb-object ('Build a winning strategy') and then details the multi-step process (finds related ended hackathons, mines winning submissions, maps to current project) and lists concrete outputs (positioning, must-ship artifacts, inspo moves, judge checklist). This clearly differentiates it from sibling tools like find_past_editions or list_winning_submissions, which are more atomic.

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 clearly states the intended use case—building a winning strategy for the current project—and provides a boundary with 'This is inspiration/strategy — adapt patterns; do not copy code or brands.' However, it does not explicitly specify when not to use this tool or name alternatives such as list_winning_submissions for simpler needs, so it stops short of explicit 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?

No annotations are present, so the description carries the full burden for behavioral disclosure. It mentions the clustering action but does not state whether the operation is read-only, what output format to expect, or any side effects. This is a significant gap for a tool that likely groups data.

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

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

The description is a single concise sentence that clearly states the core function. It contains no filler or redundant wording.

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

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

Even though an output schema exists, the description lacks essential context about the input parameter, the clustering process, or how this step fits into the broader workflow. It is too sparse for a tool that performs grouping and likely has nuanced 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?

The input schema contains one parameter (hackathon_id_or_url) with no description, and the tool description does not mention or explain this parameter. With 0% schema description coverage, the agent receives no semantic guidance about what to pass.

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 clusters public submissions by idea similarity and track family, specifying both the action and the resource. It distinguishes from siblings like extract_public_submissions and find_similar_submissions, though it doesn't explicitly contrast them.

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

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

No guidance is provided about when to use this tool versus alternatives. The description gives no context on prerequisites, typical use cases, or scenarios where another tool would be preferable.

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

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

No annotations are provided, and the description does not disclose whether re-deriving features mutates the stored profile, requires permissions, or has side effects. The term 're-derive' suggests computation but leaves unclear if it writes back to storage.

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 10-word sentence with no filler, achieving maximum conciseness. It is front-loaded with the key verb and resource.

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 an output schema, the description gives a basic purpose but omits usage context, behavioral side effects, and parameter details. It is minimally adequate for an agent to infer the tool's function but leaves gaps that other tools or schemas do not fill.

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 0% schema description coverage, the description should clarify project_id, but it does not mention the parameter at all. The only hint is 'existing stored project profile,' which loosely implies project_id identifies that profile.

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

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

The description uses the specific verb 're-derive' and identifies the resource ('existing stored project profile') and output ('structured features'). This clearly distinguishes it from creation/analysis tools, though it could specify which features are derived.

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 it is used when a project profile already exists and its features need to be recomputed, but it provides no explicit guidance on when to choose this over siblings like analyze_project or score_project_fit. No alternatives or exclusions are mentioned.

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

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

With no annotations, the description carries full behavioral burden. It discloses that since_timestamp is ignored, that the diff is against a stored watch snapshot, and that a snapshot is created if missing—an important side effect. It doesn't cover return format or auth, but the output schema likely covers return shape.

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 short sentences, front-loaded with the core function and no filler. Every sentence contributes meaningful information.

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

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

Given the output schema exists, the description sufficiently covers the tool's behavior and caveats. It lacks explicit use-case or alternative guidance, but the core purpose and MVP limitations are well stated.

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

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

Schema description coverage is 0%, so the description must compensate. It does explain since_timestamp as API-compatibility only, which adds real meaning. However, hackathon_id is left to name-based inference and is not explicitly tied to the snapshot 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?

Clearly states it diffs current public submissions against the last watch snapshot, using a specific verb (diff) and resource. This distinguishes it from sibling gallery/extraction tools like discover_public_gallery or extract_public_submissions.

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 makes its primary use clear: compare live submissions to a stored snapshot. It also warns that since_timestamp is accepted only for API compatibility and ignored in the MVP, which is useful guidance. However, it does not explicitly mention alternatives or when not to use this tool.

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

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

With no annotations, the description carries the transparency burden. It discloses important behavioral traits: search snippets are never presented as events, a candidate only appears after page fetch and evidence of hackathon content, and ingestion stores normalized metadata. This adds useful context beyond the input schema, though it omits potential rate limits or pagination details.

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 compact and front-loaded: it states the main action in the first sentence, then adds necessary behavioral clarifications in the following sentences. Every sentence contributes meaning without unnecessary verbosity.

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?

While the tool's high-level behavior is adequately described, the missing parameter semantics and lack of explicit guidance on when to use this tool over siblings create gaps. The output schema mitigates the need to document return values, but the overall usage context is only partially complete.

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

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

Schema description coverage is 0%, yet the description provides no explanation of the 'query' or 'limit' parameters. The agent must infer that 'query' is a search term and 'limit' caps results. This is insufficient given the lack of schema-level 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 that the tool discovers public event-page URLs via TinyFish Search and then verifies each one. It uses a specific verb ('Discover') and resource ('public event-page URLs'), and it distinguishes itself from siblings like search_hackathons and verify_hackathon_url by describing a combined search-and-verify pipeline.

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 a clear use case: finding and vetting hackathon event URLs. However, it does not explicitly name alternatives or state when NOT to use this tool versus siblings like search_hackathons or discover_public_gallery. The guidance is implied rather than explicit.

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 must carry the full burden of behavioral disclosure. It does add a useful constraint ('only uses intentionally public pages'), which suggests a read-only, permission-respecting behavior. However, it does not explicitly state read-only status, rate limits, or whether it makes live network requests, leaving significant behavioral traits undisclosed.

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 with no wasted words. It front-loads the core purpose and adds a relevant policy statement, achieving high information density.

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

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

Given the tool's simplicity (single parameter, output schema present) and its clear purpose, the description is mostly complete. The main gap is lack of differentiation from sibling tools like discover_hackathon_urls, but for a basic lookup tool this is acceptable.

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 named 'hackathon_id_or_url', which is self-descriptive, but the description provides zero additional meaning beyond the schema name. With 0% schema description coverage, the description fails to compensate by explaining accepted formats, examples, or the difference between ID and URL inputs.

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 a specific verb ('Find') and resource ('public project gallery') with a concrete outcome ('return its URL'). It effectively distinguishes the tool from sibling tools like discover_hackathon_urls by focusing on gallery detection specifically.

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 phrase 'Competitor intelligence only uses intentionally public pages' provides some context that this tool is intended for competitor intelligence use cases. However, it does not explicitly state when to prefer this tool over close siblings like discover_hackathon_urls or extract_public_submissions, and no exclusions or alternatives are named.

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 behavioral disclosure burden. It discloses the dependency on TinyFish, the conditions for live vs cached data, and the guarantee that auth is never bypassed. This goes beyond basic operation and provides meaningful behavioral context, though it omits error cases 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?

The description is compact and front-loaded with the core purpose. Each of the three sentences adds information: purpose, mode selection, and cache/auth behavior. No wasted words or repetition.

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

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

Given that an output schema exists and the tool has three parameters, the description covers the key behavioral aspects: live vs cached mode, dependency on TinyFish, and auth safety. It omits details about page_limit and sibling differentiation, but the overall tool context is reasonably complete for selection and invocation.

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

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

Schema description coverage is 0%, so the description must compensate. It adds meaning to use_live_extraction and mentions cached data behavior, but it does not explain page_limit or elaborate on hackathon_id_or_url beyond the schema. It partially compensates for the missing schema descriptions but leaves some parameter semantics unclear.

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

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

The description starts with a clear, specific action: 'Extract publicly visible submissions for a hackathon gallery.' This identifies the verb, resource, and scope, and differentiates from sibling tools like list_winning_submissions or discover_public_gallery by focusing on public gallery submissions rather than winners, discovery, or analysis.

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

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

The description gives useful context for parameter choices ('Uses TinyFish when TINYFISH_API_KEY is set and use_live_extraction=True', 'Cached data is available only with use_live_extraction=False'), but it does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions. It provides clear context but lacks direct guidance 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?

With no annotations, the description carries the burden of behavioral disclosure. It discloses the conditional web scraping behavior when refresh_from_web=True and FIRECRAWL_API_KEY is set, which is useful. However, it does not clarify whether 'enrich' implies writing to stored data, nor does it mention error handling or network dependency.

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

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

The description is two sentences long, front-loaded with the core purpose, and includes only necessary conditional detail. 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 simple parameter set and the existence of an output schema, the description is mostly complete. It covers the core function and the main conditional behavior. However, it leaves ambiguity about whether refresh writes to storage, which is a minor gap in context for a tool that might have side effects.

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

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

Schema description coverage is 0%, so the description must compensate. It explains the refresh_from_web parameter's effect and the external FIRECRAWL_API_KEY prerequisite. hackathon_id is not described but is self-evident from its name, so the description adds meaningful semantic 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 uses a specific verb ('Return') with a concrete resource list ('rules, eligibility, judging criteria, and artifact requirements'), which clearly distinguishes it from sibling tools like get_hackathon_details or list_tracks.

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 the tool is for retrieving rules and requirements, but does not explicitly state when to use it over alternatives or mention exclusions. The conditional refresh behavior gives some usage context, but no comparative guidance.

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

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

With no annotations provided, the description carries the full burden. It discloses the core behavioral rules: past editions share series_id, and similar events match themes/judging. This adds meaningful transparency beyond the tool name, though it does not mention side effects or permissions (which are likely irrelevant for a read-only find 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 two sentences, with the primary purpose in the first sentence and supporting details in the second. Every sentence adds value; no filler or repetition.

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

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

The presence of an output schema reduces the need to explain return values. The description covers the tool's purpose, selection criteria, and downstream usage. It is not a 5 because it leaves top_k undocumented and does not mention any limitations or preconditions, but overall it is sufficient for the tool's simple read-only nature.

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%, so the description must compensate. It implicitly explains hackathon_id (the series) and include_similar ('and/or very similar ended events'), but top_k is completely unaddressed. The semantics for two out of three parameters are only hinted at, not explicitly tied to parameter names.

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

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

The description uses a specific verb ('Find') and resource ('past editions of this hackathon series and/or very similar ended events'), clearly distinguishing it from siblings like search_hackathons. It also adds semantic detail by mentioning series_id and similar event matching.

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

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

The description gives clear context on when to use the tool (for finding past editions/similar events) and suggests a workflow ('Use results with list_winning_submissions and build_winner_playbook'). However, it does not explicitly state alternatives or when-not-to-use, so it misses the top tier.

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

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

No annotations are provided, so the description must carry the full burden. It only states the tool 'finds' neighbors, implying read-only behavior, but gives no detail on output, ranking criteria, pagination, or any side effects. This is a minimal disclosure that doesn't meaningfully inform the agent about behavioral traits.

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

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

The description is a single concise sentence that is front-loaded with the core action and object. No redundant or 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 the tool's apparent simplicity and the presence of an output schema, the description covers the basic idea. However, it lacks context about required input relationships (e.g., why both project_id and hackathon_id_or_url are needed) and does not clarify the notion of 'nearest'. For a tool with no annotations and many related siblings, this is only minimally complete.

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

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

Schema description coverage is 0%, and the description does not explain any of the three parameters. While the schema property names are somewhat self-explanatory (project_id, hackathon_id_or_url, top_k), the description adds no semantic context about their roles or relationships, such as how hackathon_id_or_url scopes the search or how top_k limits results.

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 'Find' and specific resource 'nearest public submissions to the user project', with clarifying parenthetical 'similarity neighbors'. This clearly distinguishes it from related sibling tools like extract_public_submissions or cluster_competitor_projects, which have different purposes.

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

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

No explicit when-to-use or when-not-to-use guidance is provided. The description implies usage for identifying similar public submissions relative to a user project, but does not mention alternatives or exclusions. This leaves the agent to infer the appropriate context from the tool's name and schema.

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

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

No annotations are present, so the description carries the transparency burden. 'Get' implies a read-only operation, and 'normalized metadata' plus 'source and verification state' adds useful content, but it does not disclose error handling, authorization needs, or invalid-ID behavior.

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

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

The description is a single, tightly worded sentence that conveys the core action and key output details with no filler. It is appropriately front-loaded and does not waste the reader's attention.

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 is simple (one parameter) and has an output schema, so return values are already documented. The description covers the essential purpose and distinctive output elements, though it stops short of explaining how to obtain the hackathon_id or any usage caveats. Still, for a basic getter, it is largely complete.

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

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

Schema description coverage is 0%, yet the description does not elaborate on the single parameter `hackathon_id` beyond what the schema already shows. The property name is self-explanatory, but the description fails to compensate for the lack of schema documentation by explaining the expected format or how the ID is obtained.

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 names a specific verb ('Get') and a clear resource ('full normalized metadata'), and it distinguishes the tool from siblings by noting unique attributes: 'source and verification state.' This leaves no ambiguity about 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 description provides no guidance about when to use this tool versus alternatives. It does not mention prerequisites, such as needing a hackathon_id from a search, nor does it call out cases where a sibling tool like search_hackathons or verify_hackathon_url would be more appropriate.

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

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

No annotations are provided, so the description carries the full burden. It only hints at behavior via 'job-style retrieval', which suggests asynchronous retrieval, but doesn't disclose side effects, permissions, error handling, or polling behavior. This is too thin for a tool without 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?

One concise sentence, front-loaded with the action and resource, with no wasted words or redundant details.

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

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

Given the simplicity of the tool and the existence of an output schema, the description is adequate but misses the opportunity to contextualize the run_id's origin (e.g., from run_scout_agent or list_scout_agents). It provides the essential retrieval context but lacks a pointer to the scout agent workflow.

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

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

Schema description coverage is 0% for run_id. The description merely mentions 'by run_id' without adding format, source, or relationship to other tools. It doesn't compensate for the lack of schema documentation, leaving the parameter semantics to inference from the name 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 verb 'Fetch' and the resource 'previous scout agent run' identified by run_id. It also adds 'job-style retrieval' which distinguishes it from sibling tools like run_scout_agent and list_scout_agents.

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

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

The description implies this tool is used to retrieve a completed run when you have a run_id, fitting within the scout agent workflow. It doesn't explicitly exclude alternatives or state when not to use it, but the context is clear for a simple retrieval tool.

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

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

With no annotations, the description carries the full burden. It discloses a behavioral trait: 'Without it, returns guidance and does not invent data,' which is valuable. However, it does not explain behavior on success/failure, idempotency, or what 'store normalized metadata' entails, leaving gaps in 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 two sentences with no filler. The first sentence captures the core purpose, and the second adds a critical prerequisite and fallback. Every word 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?

For a simple one-parameter tool with an output schema, the description covers essential context: the tool's function, the required API key, and the non-hallucination fallback. It could mention what happens on successful ingestion or provide more about the stored metadata, but the output schema likely covers return values.

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

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

The schema has no description for the URL parameter (0% coverage), so the description must compensate. It clarifies that the URL is a hackathon landing page, which adds meaning beyond the bare schema. However, it offers no format constraints, examples, or further details, so compensation is partial.

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

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

The description clearly states the action: 'Ingest a hackathon landing page via Firecrawl and store normalized metadata.' It uses a specific verb ('ingest'), resource ('hackathon landing page'), and outcome ('store normalized metadata'), distinguishing it from sibling tools like get_hackathon_details or verify_hackathon_url.

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. The only stated condition is the FIRECRAWL_API_KEY requirement, which is a prerequisite, not usage context. It does not mention scenarios, exclusions, or 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?

No annotations are provided, but the description discloses that each agent has scoped capabilities, modes (quick/deep/oracle), and returns phased events + citations + a structured report. This adds behavioral context beyond a bare listing, though it doesn't mention any side effects or security requirements. For a parameterless list operation, this is adequate.

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

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

The description is two sentences, front-loaded with the purpose, followed by usage guidance and agent characteristics. Every sentence is informative with no filler, making it highly concise 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?

Given the tool's simplicity (no parameters) and the presence of an output schema, the description fully covers what the tool does and why an agent would use it. It also connects to the broader context of multi-step goals, making it complete for its purpose.

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 zero parameters and 100% schema coverage, the baseline is 4. The description correctly doesn't attempt to explain parameters because there are none, so it adds no unnecessary 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 specifies the action ('List') and resource ('specialized HackScout scout agents'), adding the Nia-inspired qualifier. This distinguishes it from sibling tools like run_scout_agent or get_scout_run, which are related but have different purposes.

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

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

It explicitly recommends these agents for multi-step goals instead of manually chaining atomic tools, providing clear usage context. However, it doesn't name alternative listing tools or explicitly state when not to use this list, so it lacks explicit 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?

No annotations are provided, so the description should disclose behavioral traits. It only states the basic action and does not mention pagination, ordering, authentication, or any side effects. The description adds no context beyond the tool's name and schema.

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, clear sentence with no padding. It earns its place, though it is slightly under-specified for what could be a more informative one-liner.

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 an output schema present, return values do not need elaboration. The tool has only one parameter and low complexity. However, the lack of usage guidance and behavioral transparency leaves gaps, making it only minimally viable for an agent to decide when 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 description coverage is 0%, so the description must compensate. It implies hackathon_id is the target, but does not explain its format, whether it is an ID or URL, or how it is used. The description adds minimal meaning beyond the schema field name.

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

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

The description clearly identifies the action (list) and the resource (tracks and sponsor challenges for a hackathon). It does not explicitly differentiate from sibling tools like get_hackathon_details, which could also list tracks, but the specific mention of sponsor challenges adds a distinguishing element.

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 given for when to use this tool versus alternatives. The sibling tools include get_hackathon_details and suggest_track_strategy, which may overlap, but the description does not mention exclusions or preferred contexts.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that only publicly announced winners are returned, that past editions are preferred, and lists the return fields. However, it does not clarify whether the operation is read-only, if authentication is needed, or how 'prefer' affects results (e.g., fallback behavior). This is adequate but not rich.

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

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

The description is two sentences, front-loaded with the primary purpose and supplemented by a terse second sentence covering usage nuance and return fields. No waste, no redundancy, ideal size.

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 a single parameter, an output schema (signaled), and a simple listing purpose. The description covers the key behavioral context (public availability, past editions, return contents) and is sufficient for the likely use case. It does not explicitly differentiate from all sibling tools, but within this context it is largely 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?

The schema provides only a parameter name (hackathon_id) with no description (coverage 0%), so the description must compensate. It does add meaning by tying the parameter to 'a hackathon' and advising to prefer past editions, which informs the choice of hackathon_id. However, it does not explain the format, source, or how to obtain the ID, so it only partially compensates for the schema gap.

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 lists publicly announced winners/awarded submissions for a hackathon, with a specific verb ('List') and resource ('winning submissions'). It also distinguishes itself by mentioning the specific return fields (award, placement, win_signals, links), which differentiates it from broader submission-listing tools like extract_public_submissions.

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

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

The description gives clear usage context by advising to prefer past (ended) editions, which guides the agent to use this tool for historical hackathons rather than ongoing ones. It does not explicitly name alternatives or exclusions, but the sibling names (e.g., extract_public_submissions) imply a contrast. This warrants a 4, as it provides context but no explicit when-not-to-use.

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

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

With no annotations, the description must carry the behavioral disclosure burden. It does disclose that only 'verified, fresh upcoming/ongoing' hackathons are ranked and that include_demo is for local walkthrough fixtures only. It does not disclose how ranking is computed, whether it uses the project profile, or any side effects, but the tool appears to be a read-only ranking 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?

Two short sentences, front-loaded with the primary purpose and followed by a precise usage note. Every word earns its place; there is no fluff or repetition.

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

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

Given the presence of an output schema, the description does not need to explain return structure. It covers the tool's purpose, the filtering criteria ('verified', 'fresh', 'upcoming/ongoing'), and the special include_demo case. It could be more complete by stating how 'best' is determined or that project_id is required, but these are minor given the 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?

The schema has 0% description coverage, and the description only explains include_demo behavior. It does not clarify the meaning of project_id or top_k beyond their names/defaults. Because the schema property names are reasonably self-explanatory, this is still somewhat usable, but the description leaves important semantic gaps.

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

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

The description uses a specific verb ('Rank') and a clear resource ('verified, fresh upcoming/ongoing hackathons for a project'), immediately distinguishing this from sibling tools like search_hackathons or get_hackathon_details. It states the core function and scope in one sentence.

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 clearly implies this tool is for obtaining a ranked list of currently relevant hackathons for a project. It also supplies a specific usage caveat ('Set include_demo=True only for the bundled local walkthrough fixtures'), which helps prevent misuse. However, it does not explicitly contrast with alternatives like score_project_fit or search_hackathons.

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 must carry the full burden of behavioral disclosure. It does disclose a dependency on extract_public_submissions and TINYFISH_API_KEY for live re-extraction, but it does not explain what 'refresh' does concretely (e.g., overwrites stored data, is idempotent, or any side effects). The mention of 'stored' implies an update but lacks detail.

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 short sentences, front-loaded with the main purpose and followed by a necessary caveat. Every sentence earns its place; no fluff or repetition of schema fields.

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 is simple (one parameter, no nested objects, output schema exists), and the description covers the primary purpose and an important dependency. It does not explain what 'gallery snapshot' means, but that is likely domain knowledge. The note about extract_public_submissions adds necessary context for correct invocation.

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

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

The only parameter, hackathon_id, has a self-explanatory name but the schema provides no description (0% schema coverage). The description adds minimal additional meaning by noting 'watched (or any) hackathon', which clarifies that it applies broadly, but it does not explain the format or expected value of hackathon_id. With low schema coverage, the description should compensate more.

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

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

The description clearly states the action ('Refresh') and the target resource ('the stored gallery snapshot') for a specific scope ('a watched (or any) hackathon'). The verb 'refresh' is distinct from sibling tools like extract_public_submissions, which implies extraction rather than updating an existing stored state.

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 context that this works for watched or any hackathon, and includes a prerequisite note: live re-extraction requires calling extract_public_submissions first when TINYFISH_API_KEY is configured. This implies when to use this tool versus extract_public_submissions, though it doesn't explicitly state 'use this when you want to update an existing snapshot without full re-extraction'.

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 transparency burden. It discloses that the tool spawns an agent, returns a phased/cited report, and explains the meaning of quick/deep/oracle modes. However, it omits operational traits such as whether runs are synchronous, side effects, permissions, or ambiguity in gallery_intel's 'deploy into a public gallery' behavior.

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

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

The description is front-loaded with a one-sentence summary, followed by well-formatted bullet lists for agents and modes. Every line earns its place; the 'Returns' line and the pointer to get_scout_run are useful and not redundant.

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

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

The description covers the fundamental choices (agent, mode), what the tool returns, and how to retrieve results later, which is strong for an orchestrator. Since an output schema exists, omitting full return-field detail is acceptable, but the absence of guidance on how project_id/hackathon_id constrain the run is a notable 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 description coverage is 0%, so the description must compensate. It does explain the key enum values (agent_id and mode), but goal, query, top_k, enrich, project_id, and hackathon_id are entirely left to inference. For an 8-parameter tool, this leaves significant invocation ambiguity.

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

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

The description opens with a specific verb and resource: 'Spawn a specialized scout agent and return its phased, cited report.' It then enumerates the exact agent variants and modes, making the tool's function unmistakable and distinguishing it from sibling get_scout_run by noting the report can be retrieved later.

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 clearly maps agent IDs to their purposes and defines the three mode depths, giving an agent strong contextual cues for when to invoke this orchestrator. It also names an explicit alternative for retrieval ('Retrieve later via get_scout_run'), but it does not explicitly state when to prefer this over the many other domain 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?

With no annotations provided, the description carries the full burden for behavioral disclosure. It only says 'Estimate,' which suggests a read-only analysis, but it doesn't clarify whether the tool accesses external data, has side effects, or requires permissions. No contradictions, but insufficient 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?

One sentence, front-loaded with the verb, contains no filler. However, it is too terse to fully serve its purpose, but that is a completeness issue, not a conciseness issue. Score 4.

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, combined with the schema, leaves many gaps: no parameter meanings, no annotation baseline, no context on when to use this versus score_project_fit or cluster_competitor_projects. The output schema exists but doesn't compensate for the missing behavioral and parameter information. Score 2.

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 two required parameters (project_id, hackathon_id_or_url) with zero description coverage, and the tool description does not mention them at all. It fails to clarify what these parameters represent or how they map to 'the project' or 'public gallery,' scoring 1.

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

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

The description uses the specific verb 'Estimate' and clearly identifies the resource: crowding/novelty risk for a project in a public gallery. It is distinct from siblings like score_project_fit by focusing on crowding, though it doesn't explicitly name alternatives, so it falls just short of a 5.

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 the tool is used when assessing a project within a public gallery context, but it provides no when-not-to-use guidance or references to alternatives among the 27 sibling tools. It stops at stating the action without contextual boundaries.

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. It discloses the exact weighted formula and says it provides a dimension breakdown and explanation, which is useful behavioral transparency. It does not mention side effects or permissions, but as a scoring function it is likely safe; still, more detail about the 'enrich' parameter's effect would improve it.

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

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

The description is two sentences: a clear purpose statement followed by the formula. It is front-loaded and each sentence provides essential information with no filler.

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

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

Given an output schema exists, the description need not explain return structure, but it still lacks usage context and parameter meaning. The formula and breakdown are well covered, yet the tool's role among sibling tools and the effect of the 'enrich' flag remain undefined, making it adequate but incomplete for an agent to fully 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 description coverage is 0%, so the description must compensate. It does not explain project_id, hackathon_id, or enrich beyond their names. The formula mentions dimensions like theme and stack, but these are not parameters and do not clarify the input parameters' semantics or expected formats.

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 scores project fit for a hackathon with a dimension breakdown and explanation. It names a specific verb ('score') and resource ('project fit vs hackathon'), which distinguishes it from general analysis tools like analyze_project, though it does not explicitly reference sibling tools to differentiate them.

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

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

No explicit guidance on when to use this tool versus alternatives like rank_best_targets or analyze_project. The description states what it does but omits context like prerequisites, exclusions, or when this scoring is preferred.

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. It transparently mentions 'verified, fresh' data and explicitly warns that include_demo adds synthetic fixtures for local demos only. It does not discuss side effects or auth, but these are less critical for a search tool.

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

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

The description is a single-sentence summary followed by a compact bullet list of parameter explanations. Every line adds value with 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?

All seven parameters are explained, and the presence of an output schema covers return values. The description could mention pagination or result ordering, but these are not critical for a search tool and the current level is sufficient.

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

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

The description provides detailed semantics for every parameter, including allowed enum values for date_range and mode, the meaning of stack, geography, prize_min, and include_demo. This fully compensates for the schema's lack of 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 action ('Search') and the resource ('verified, fresh upcoming/ongoing hackathons'), and distinguishes this from sibling tools like get_hackathon_details or discover_hackathon_urls by focusing on the filtered search capability.

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 what filters are available and their semantics, giving clear context for when to use this tool. However, it does not explicitly state when not to use it or mention alternatives, which would strengthen the guidance.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'suggest', implying a non-mutating analytical action, but does not disclose dependencies, data sources, side effects, or whether prior steps (like extracting the public submission landscape) are required.

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, front-loaded sentence with no wasted words. It is concise and readable, but it is perhaps too terse given the tool's complexity and the need for usage guidance.

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

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

The tool has an output schema (not shown) but the description does not explain what the output looks like, what inputs are expected, or what preconditions must be met. With no annotations and a complex sibling ecosystem, this description is incomplete for reliable tool selection and invocation.

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

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

Schema description coverage is 0%, and the description does not explain project_id or hackathon_id_or_url. While the parameter names are somewhat self-explanatory, the description adds minimal meaning beyond the schema and does not compensate for the lack of parameter documentation.

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 with a specific verb ('Suggest') and object ('concrete differentiation moves'), and provides context ('based on public submission landscape'). However, it does not distinguish itself from related sibling tools like suggest_track_strategy or cluster_competitor_projects, so it misses the top score.

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

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

The description gives no guidance on when to use this tool versus alternatives. With many sibling tools in the same domain, such as extract_public_submissions, cluster_competitor_projects, or suggest_track_strategy, an agent would have no basis for choosing this tool.

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

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

No annotations are provided, so the description must disclose behavioral traits. It says 'Recommend' but does not clarify whether this is a read-only analysis, what inputs are required, what the output format is, or any side effects. The description is too terse to convey meaningful behavioral context for a recommendation tool.

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

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

The description is a single concise sentence with no filler. It is front-loaded and easy to parse. However, its brevity comes at the cost of completeness, though as a standalone statement it is efficiently written.

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 an output schema, so return value details are not necessary, but the description lacks important context: how the recommendation is derived, what inputs are needed, and how it relates to other pipeline tools. For a tool with two required parameters and no annotation support, the description is too sparse to fully prepare an agent.

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

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

Schema description coverage is 0%, and the description does not explain the two parameters (project_id, hackathon_id) beyond their names. Though the parameter names are somewhat self-explanatory, the description adds no semantic detail about what each means or how they are used in the recommendation process.

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

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

The description uses a specific verb ('Recommend') and names a clear resource: the best track/sponsor challenge and a positioning strategy. It distinguishes itself from sibling tools by combining track selection with positioning, though it is somewhat ambiguous relative to rank_best_targets and suggest_differentiation_moves.

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?

There is no guidance on when to use this tool versus alternatives, no prerequisites, and no mention of whether it should be used before or after other tools like derive_project_profile or score_project_fit. The description simply states what it does, leaving usage context entirely implicit.

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

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

No annotations are present, so the description carries the full burden. It discloses key behaviors: it actually visits a public URL, requires page evidence, avoids URL-shape inference, and if hackathon_id points to a non-demo stored record, it updates that record's verification state. This is significant, especially the side-effect of state mutation. Minor omissions like failure modes or network/rate limits exist, but the description does well given the absent annotations.

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

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

The description is two sentences: the first states the primary purpose and the second adds a key behavioral constraint and side-effect. It is well-structured, front-loaded, and contains no extraneous information. 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?

For a tool with 2 parameters, no annotations, but an output schema, the description covers the essential behavioral contract: what it does (visit and require evidence), a key rule (no URL-shape inference), and a side-effect (state update). It does not need to explain return values due to the existing output schema. Could elaborate on evidence criteria or failure behavior, but overall it is sufficiently complete.

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

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

Schema description coverage is 0%, so the description must compensate. It partially does: 'url' is not explained beyond 'Visit a public URL,' and 'hackathon_id' is given context (named non-demo stored record; verification state updated). However, the meaning of 'non-demo stored record' is unclear, and 'url' lacks specifics like full URL requirements. The description adds some value but does not fully compensate for the zero-coverage 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 core action: 'Visit a public URL and require page evidence before trusting it as a hackathon.' This is a specific verb (verify/visit) plus the resource (URL) and the criterion (page evidence). It also differentiates from sibling tools like discover_hackathon_urls and ingest_hackathon_url by emphasizing it never infers validity from URL shape, making its purpose distinct.

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?

Usage is implied: use when you need to verify whether a URL is a hackathon by actually visiting it. However, there is no explicit guidance about when not to use this tool or which alternatives might be better suited (e.g., discover_hackathon_urls for discovery). The statement about not inferring from URL shape is a behavioral constraint, not a usage directive.

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

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

No annotations are provided, so the description carries full responsibility for disclosing behavior. It states that the tool adds to the watchlist and stores an initial gallery snapshot, which implies a mutation, but it does not cover idempotency, side effects, prerequisites, or error behavior. The description gives only a high-level action without operational 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 that conveys the core purpose without any fluff or redundancy. It is well-structured and front-loaded, making it easy to parse quickly.

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 role in a broader workflow (e.g., after ingest_hackathon_url or get_hackathon_details), the description lacks contextual cues about its place in the process. It does not mention prerequisites, follow-up actions, or how the stored snapshot will be used later. While an output schema exists, the description still fails to provide workflow-level 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?

The schema has one required string parameter, hackathon_id, with 0% description coverage. The description does not mention the parameter at all, leaving the agent to infer its meaning from the name alone. This is insufficient because the description should at least indicate where to obtain the ID or what it represents.

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 specific verb 'Add', the resource 'a hackathon', and the target 'the watchlist', and also mentions 'store an initial gallery snapshot', which distinguishes it from sibling tools like get_hackathon_details or refresh_gallery_state. This clearly communicates the tool's core function.

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

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

There is no guidance on when to use this tool, when not to use it, or how it relates to alternatives such as refresh_gallery_state or get_hackathon_details. The intended usage is implied by the name and description but not explicitly stated, so context is missing.

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