Agent Data Co

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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds useful behavioral context beyond that: daily refresh, API key requirement, and queuing behavior for untracked pages. No contradictions with annotations.

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

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

Four short sentences, front-loaded with the tool's purpose and key behaviors. No wasted words, though parentheticals add slight density. Well-structured for quick scanning.

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 the source, freshness, and auth requirement, but omits return format, pagination details (limit/offset behavior), and filtering specifics for q and since. For a 5-parameter tool with no output schema, more context would be needed to fully aid 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?

Schema description coverage is 40%, so the description must compensate. It adds meaning for the url parameter (pass url= for one page, untracked pages queued) but leaves q, since, limit, and offset unexplained beyond their schema types and defaults. Partial compensation only.

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?

States a specific resource (AI & SaaS pricing page snapshots) and source (public pricing pages), making the tool's data clear. However, it does not explicitly distinguish itself from the sibling get_llm_pricing, which likely covers overlapping pricing data, so an agent might hesitate between 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?

Provides a specific usage instruction for the url parameter (pass url= for one pricing page), which is helpful. But it gives no guidance on when to use this tool versus alternatives like get_llm_pricing, nor any exclusions or contextual triggers.

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

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

Annotations already establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful context beyond that: the data source (GitHub Search API), daily refresh cadence, API-key requirement, and the side-effect that untracked repos get queued when passed via repo.

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 short sentences, front-loaded with what the tool returns before moving to source, freshness, auth, and the single-repo mode. Dense but no filler; the parenthetical source note is the only mildly extraneous element.

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 no-required-param, no-output-schema feed tool, the description covers source, freshness, auth, and the single-repo path. The main omission is pagination behavior (limit/offset) for the full feed, which an agent would need to page results correctly.

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

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

Schema coverage is only 40%, with q and since documented but repo, limit, and offset bare. The description compensates for repo by explaining the owner/name format and queueing, but says nothing about limit/offset pagination semantics, leaving half the parameters unexplained in both places.

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 resource and verb: a feed of GitHub AI agent & MCP repos sourced from the GitHub Search API, refreshed daily. It differentiates reasonably from siblings like get_mcp_servers and get_trends, though it doesn't explicitly contrast with 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?

It gives one concrete usage pattern (pass repo=owner/name to fetch a single repo) and notes the queuing behavior for untracked repos. However, there is no guidance on when to use this versus the other feed/pricing/lookup siblings, and no exclusions or prerequisites beyond the API key note.

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

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

Annotations already declare readOnlyHint and openWorldHint, so the bar is low, and the description still adds two useful behavioral facts: the feed is refreshed daily and an API key is required. It does not disclose rate limits, pagination behavior, or what an empty filter result looks like, keeping it out of 5 territory.

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

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

Three short front-loaded fragments with no filler; the resource and source lead, constraints trail. Slightly telegraphic ('Requires API key' as an orphan fragment) but every sentence earns its place.

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

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

With no output schema, the description should carry more of the burden, yet it omits the return shape, whether results are ranked or paginated, and how filters interact. It does supply the minimum needed to call the tool: what it returns, how fresh it is, and the auth requirement.

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

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

Schema description coverage is only 50% (limit and offset are undocumented) and the description adds nothing about q, since, limit, or offset. 'Full current feed' hints at an unfiltered default but gives no syntax, format, or pagination meaning to compensate for the 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 first sentence names a specific resource (Hugging Face trending models & Spaces) and its provenance (Hugging Face Hub API), which is enough to separate it from siblings like get_trends or get_momentum_leaderboard. It does not explicitly contrast with those siblings, so it falls 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?

There is no statement of when to pick this tool over get_trends, get_momentum_leaderboard, or get_github_ai_repos, nor any exclusions. The closest thing to guidance is 'Full current feed, refreshed daily,' which describes the data, not the selection condition.

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

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

Annotations only declare read-only and closed-world; the description adds substantial behavioral context beyond them: append-only archive, Starter/Pro tier limits, 90-day window cap, per-page row limit, daily fair-use call quotas, 1-hour throttling with 429, bulk licensing, and API-key requirement.

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

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

Front-loads purpose, then presents constraints (window, pagination, rate limits, licensing, auth) in a compact block. Every sentence adds operative information for correct invocation; 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?

For a read-only tool with no output schema, the definition covers access scope, pagination, rate limits, tiering, licensing, and auth. It lacks explicit return-field detail and does not route to close siblings like lookup_entity, so it is strong but not perfect.

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 67%, so schema already documents most parameter details. The description adds meaningful constraints beyond the schema: 'ONE entity' (singular scope), explicit 90-day from/to window, cursor pagination flow, and an effective page-row cap; feed enum semantics remain only in 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?

States a specific verb and resource: 'Daily time series for ONE entity from the append-only archive.' The 'ONE entity' and 'No whole-feed dumps' wording cleanly distinguishes it from whole-feed siblings like get_trends or get_momentum_leaderboard.

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?

Gives clear when-to-use constraints (max 90-day window, pagination via cursor, no whole-feed dumps, tier caps) and when-not (bulk access requires separate licence). Does not explicitly name an alternative sibling tool to use instead, so falls short of a 5.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds two genuinely useful behavioral facts beyond the annotations: an API key is required, and the feed is refreshed daily (so results may be up to a day stale). It still says nothing about pagination behavior or result size, so it is adequate rather than 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?

Three compact fragments, front-loaded with the resource and source, with no filler. The parenthetical '(source: OpenRouter models API)' is slightly redundant with the preceding parenthetical, but the total length is well matched to the tool's simplicity.

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

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

With no output schema, the description has to carry the return-value burden, and 'Full current feed' only gestures at what comes back (pricing records). For a four-parameter, read-only list tool whose annotations and schema cover safety and arguments, this is minimally sufficient but leaves return format and paging unspecified.

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 50%: 'q' and 'since' are documented in the schema, while 'limit' and 'offset' are self-explanatory from their defaults and bounds. The description contributes no parameter meaning at all, so it neither compensates for nor exceeds the schema — baseline 3 for a mostly self-documenting parameter set.

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 the resource precisely (LLM model pricing from the OpenRouter models API) and states the scope is the full current feed. It is clear what the tool returns, but it never distinguishes itself from the sibling get_ai_pricing, so an agent cannot tell which pricing tool applies without opening both schemas.

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 when-to-use / when-not-to-use guidance and no mention of alternatives. 'Full current feed, refreshed daily' describes the data, not the conditions under which an agent should call this instead of get_ai_pricing or a cached lookup.

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

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

Annotations already cover safety (readOnlyHint=true, openWorldHint=false), yet the description adds genuinely operational facts beyond them: the data source domain, a daily refresh cadence, and an API-key requirement. It omits return shape and pagination behavior, so it is not fully transparent.

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

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

Three short sentences, front-loaded with the resource and source, then scope/freshness, then the auth prerequisite. No filler and nothing redundant with the schema.

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

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

With no output schema and only partial parameter documentation, the description should say more about what a returned server record contains and how paging works (limit/offset). It covers source and freshness adequately but leaves the agent guessing about the response.

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 50% (q and since documented; limit and offset undocumented), and the description says nothing about any parameter, so it fails to compensate for the gap. It does not clarify filtering, date semantics, or the 1000-item limit.

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 resource (MCP servers from the official registry.modelcontextprotocol.io) and its scope (full current feed), which cleanly separates it from pricing/repo/trend siblings. It never states the verb explicitly (list/retrieve) but the intent is unambiguous.

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

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

"Full current feed" implies this is the browse-everything call rather than a filtered lookup, and "Requires API key" flags a prerequisite, but no when-to-use condition, alternative, or exclusion is stated. The agent must infer usage from context.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds real context beyond that: it is free, the data comes from a daily archive, and the score is a composite of four named signals. It still omits pagination, response shape, and whether the Agent Demand Index is always returned or only for certain feeds.

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 dense but front-loaded sentence that leads with the tool's identity and score definition before listing coverage. Nothing is redundant, though the nested parenthetical makes it heavier than necessary for a two-parameter tool.

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

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

With no output schema and no annotation detail on returns, the description must explain what comes back; it does name the leaderboard and Agent Demand Index, but not the item shape, ordering, or whether the Agent Demand Index is coupled to the feed selection. Adequate for invocation, thin for interpreting results.

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 carries the full burden. It partially compensates by enumerating the four content categories that map to the feed enum (github-ai-repos, mcp-servers, llm-pricing, hf-trending), though the LLM/pricing mapping is loose. The limit parameter (1-100, default 25) is not mentioned at all, leaving a real 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 names the resource (ADC Momentum leaderboard) and the exact content it returns: a 0-100 score derived from growth velocity, consistency, recency and agent demand, plus an Agent Demand Index. The coverage list (GitHub AI repos, MCP servers, LLMs, Hugging Face items) overlaps with siblings like get_github_ai_repos and get_mcp_servers, but the momentum-score framing distinguishes it. It stops short of explicitly contrasting itself with those siblings.

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?

Only the 'Free:' prefix hints at a reason to prefer this over paid siblings; there is no statement of when to use a leaderboard versus get_trends, get_github_ai_repos or get_mcp_servers. No prerequisites, exclusions or alternative-routing guidance are given. The agent must infer usage from the description alone.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuinely useful behavioral context beyond that: the result is capped at 5 rows, and no authentication/API key is required, which tells the agent it can call this without credentials. Return shape details are minimal but adequate for such a simple read.

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 clauses, zero waste, front-loaded with the sample size and scope. Every phrase 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 no-auth, one-optional-param read tool with no output schema, the description covers the essentials: what is returned (5 latest rows), from what (one or all feeds), and that no key is needed. It could be stronger by naming an alternative tool, but nothing critical is 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 single feed parameter has 0% schema description coverage and only an enum of values, so the description must carry the meaning. 'one or all feeds' clarifies that the parameter is optional and that omitting it returns all feeds, which the enum alone does not convey.

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?

States a specific verb+resource+scope: retrieve a free sample of the 5 latest rows from feeds. This differentiates it from siblings like get_github_ai_repos, which presumably return fuller data sets. It stops short of explicitly naming an alternative, but the scope ('sample', '5 latest rows') is unambiguous.

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

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

'No API key needed' implies a context for use (free/trial access without authentication), and 'one or all feeds' hints at the optional feed argument. However, it never states when to prefer this over get_github_ai_repos or list_feeds, so the agent must infer the choice from the word 'sample'.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuinely useful behavioral context: it is a complete (not paginated-by-default) feed, it refreshes daily so freshness expectations are set, and it requires an API key, which is an auth requirement the annotations do not 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?

Three tight sentences with the resource and scope front-loaded and no filler. It is efficient, though the trailing 'Requires API key' reads like an afterthought rather than integrated 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?

With no output schema, the description carries the burden of explaining what comes back, and it does not (item shape, fields, ordering). It also never explains how 'full feed' interacts with the limit/offset parameters available in the schema, leaving a real gap 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?

Schema coverage is only 50%: 'q' and 'since' are documented in the schema while 'limit' and 'offset' are not. The description adds no parameter guidance at all, so the two undocumented pagination parameters are left to inference — though they are largely self-evident integers with defaults, which keeps this at minimum-viable rather than poor.

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+resource ('Show HN AI launches') and identifies the upstream source (Algolia HN Search API), which cleanly separates it from siblings like get_github_ai_repos, get_hf_trending and get_mcp_servers. It stops short of explicitly contrasting itself with an alternative tool, which is the only thing keeping it from 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?

'Full current feed, refreshed daily' implies this is the unfiltered baseline that per-query siblings narrow down, but the description never states when to reach for this tool instead of the other feed tools. No exclusions or prerequisites beyond the API-key note.

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

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

Annotations cover the safety profile (readOnlyHint, openWorldHint=false), so the bar is lower, yet the description still adds meaningful operational context: it counts against a daily fair-use cap with concrete tier limits (200 Starter / 1,000 Pro), requires an API key, and constrains past dates to plan history. Those are exactly the non-obvious traits an agent needs before invoking.

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

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

Dense, front-loaded and largely waste-free: output scope first, then defaults/dependencies, then cost and auth. Every sentence carries information, though the change-event list is somewhat enumerative and could compress.

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 read-only aggregation tool with no output schema, the description is nearly complete: it defines the returned content, the default behavior, the date dependency, cost, and auth. The main gap is feed enumeration meaning, which no output schema exists to cover.

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

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

Schema description coverage is only 33% — just the date field is annotated. The description partially compensates by naming the window values (1d/7d/30d) and stating the date-requires-feed dependency, but it never explains what the six feed enum values select, so half the parameters remain semantically thin.

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?

It states a specific output: top movers by stars/likes/points across 1d/7d/30d plus named change-event types. That's concrete verb+resource. It does not, however, distinguish itself from near-neighbors like get_momentum_leaderboard or get_history, so an agent must guess which 'movers' tool is authoritative.

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 gives real usage constraints — 'Latest by default; past dates need feed and must be within your plan history' — which tell the agent how to call it. But it never says when to prefer this over get_history, get_momentum_leaderboard, or the per-feed getters, leaving alternative selection implied at best.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds the key behavioral detail that the tool is 'Free, no API key,' which is not in the annotations and is useful for agents deciding whether to call it. It could mention rate limits or response format, but for a simple list endpoint this is good enough.

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 that front-loads the action and resource, followed by a short clarifying clause and a separate benefit statement. Zero waste.

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

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

Given the tool has no parameters, no output schema, and annotations covering safety, the description provides enough to understand what it returns (feeds, sources, update times) and that it's free. It might benefit from noting that it returns a list of feed identifiers usable with other tools, but it's 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?

There are zero parameters, so the baseline is 4. The description appropriately doesn't discuss parameters, and schema coverage is 100% (trivially). No missing information.

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?

States a specific verb (List) and resource (Agent Data Co feeds) and clarifies what the feeds contain (AI-ecosystem datasets). This clearly distinguishes it from siblings like get_ai_pricing or get_github_ai_repos, which are more specific data endpoints, but it doesn't explicitly say it's a catalog/discovery tool for the other endpoints.

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 is a discovery/catalog endpoint by listing what feeds exist, and the phrase 'Free, no API key' gives a usage constraint. However, there is no explicit when-to-use guidance or mention of alternatives among siblings, leaving the agent to infer its role.

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

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

Annotations already declare readOnlyHint and openWorldHint=false, so safety is covered. The description adds real context beyond that: the free tier, the ~2,000-entity coverage ceiling, the recency window (recent daily values), and the paid escalation path. It stops short of noting rate limits or auth mechanics for the keyed path.

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 compact sentences, no filler. The free/coverage constraint and entity formats come first, and the get_history routing note is placed last where it reads as a natural escalation hint.

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

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

No output schema exists, yet the description tells the agent the shape of the return (latest metrics + recent daily values) without overclaiming precision. Combined with coverage limits and entity formats, an agent has enough to invoke correctly; the only soft spot is the unexplained feed enum.

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 must carry the load, and it largely does: it enumerates the accepted entity forms (GitHub owner/name, MCP server name, OpenRouter model id, pricing page URL, hf "<type>:<id>"). The feed enum values go unexplained, though several are self-describing and map to the listed entity types.

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?

States a specific verb (lookup) and resource (one tracked entity) plus the exact scope of what is returned: latest public metrics and recent daily values. It also scopes coverage to the top ~2,000 entities, so an agent immediately knows what this tool will and won't answer.

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

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

Explicitly frames this as the free/latest path and names the alternative (get_history) together with the condition that selects it: full history needs a key. That is an when-to-use plus an alternative, which is exactly what the guidelines dimension asks for.

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