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

No annotations are provided, so the description carries the burden. It discloses that the tool returns full text and metadata and positions it as a follow-up to search, but it does not state read-only behavior, error handling, or any access constraints.

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 efficient sentence with the key purpose and usage cue front-loaded. There is no redundant or wasted text.

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 two-parameter retrieval tool with no output schema, the description adequately describes the return content and usage context. However, it leaves required parameter meanings and basic behavioral expectations unstated, which is a clear 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%, and the description does not explain what channel or msg_id mean or how they identify the post. It only implies that the parameters come from a prior search_posts result, leaving the parameter semantics largely undocumented.

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

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

The description states the tool returns the full text and metadata of one post, clearly differentiating it from search_posts, which only returns snippets. The purpose is evident, though it relies on the tool name for the explicit retrieval verb.

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

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

It explicitly says to use this after search_posts returns a snippet, giving clear context for when it is appropriate. It does not cover exclusions or other alternatives, but the primary workflow is well signaled.

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 and no output schema, so the description carries the disclosure burden; it does usefully enumerate what the response contains (descriptions and post counts for topics, top hashtags). It does not state that this is a read-only operation, whether it is paginated or cached, or how large the result set may be.

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, return payload front-loaded and the recommended action second; nothing is wasted.

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 annotations and no output schema, the description does cover the return shape adequately, but it leaves the only parameter unexplained and gives no sense of result size or ordering, which matters for a discovery tool feeding search_posts.

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

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

The single parameter top_hashtags has 0% schema description coverage and a default of 150. The description's phrase 'most used hashtags' only obliquely implies this is a count/limit parameter and never states that the integer controls how many hashtags are returned.

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 is a noun list of returned data ('topics, post kinds, availability values... plus hashtags') rather than a verb+resource statement, so the agent must infer this is a lookup/list tool. The second sentence clarifies its role as a filter-discovery helper, which rescues it from tautology, but it is not a clean purpose statement.

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?

'Call this to pick filters for search_posts' explicitly names the downstream tool and the condition that selects this one over get_post, recent, or stats. It stops short of stating when not to use it or whether it must be called before search_posts.

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 burden. It discloses newest-first ordering and optional filtering, but omits read-only confirmation, pagination/limit behavior, and any auth or rate-limit context.

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

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

Two sentences with no wasted text. The core behavior is front-loaded, and the example use case is brief and helpful.

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 output schema covers return values, and the description conveys the main retrieval behavior. Still, with no annotations and no schema parameter descriptions, the omitted limit/default semantics leave meaningful gaps for a simple but four-parameter tool.

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

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

Schema description coverage is 0%, so the description must compensate for all four parameters. It mentions the days window and topics/channels filters, but does not explain the limit parameter, default values, or the expected array format for topics/channels.

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 resource and scope: posts from the last N days, newest first, with optional topic/channel filters. It does not explicitly distinguish this from the sibling search_posts tool, which keeps 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?

The example 'what happened this week in agents' implies a useful recency-based use case, and the optional filters suggest narrowing by topic/channel. However, it does not say when to use this instead of search_posts or other siblings, nor does it state 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, the description carries the full burden; it does disclose exclusive-until semantics, cross-language query handling, and the returned fields. It says nothing about the default/limit result cap, ranking or ordering, or pagination behavior, which matter 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?

Front-loads the one-line purpose, then a tight per-parameter list, then the return shape. Nearly every line carries information an agent cannot get from 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?

Given 9 parameters, zero schema descriptions, and an output schema that covers return values, the description is close to sufficient. The only real hole is `limit` and result-cap 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?

Schema coverage is 0%, so the description must compensate, and it does for 8 of 9 parameters: it gives the semantics (any-of vs single value), formats (ISO dates, until exclusive), valid-value sources (list_topics), and examples ('Qwen3', arXiv ids). The `limit` parameter is not addressed at all.

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?

Opens with a specific verb and resource plus the retrieval method: 'Hybrid keyword + semantic search over all archived posts.' That is clearly distinct from get_post (single item) and recent (latest feed), though no sibling is named explicitly.

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?

Explains what each filter is for and when to prefer it ('hashtags ... very precise', 'availability ... for things you can run locally'), and routes to list_topics() for valid values. It does not state when to use this instead of `recent`, leaving that to inference.

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

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

No annotations exist, so the description carries the burden. It does disclose the kind of data produced (counts, date range, enrichment progress), which implies a non-destructive read, but it never states read-only behavior, scope of aggregation, or whether 'enrichment progress' indicates an in-flight background job.

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

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

A single compact phrase with no filler, and the most informative element (per-channel post counts) is front-loaded. It is terse to the point of under-specification, which slightly caps the score.

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 and no parameters, the definition doesn't need to explain return values, and it names the key metrics. However, it omits the aggregation scope (all channels vs. a default window) that an agent needs to interpret the numbers correctly.

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

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies.

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

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

The fragment names specific returned content (per-channel post counts, date range, enrichment progress), which lets an agent distinguish it from get_post/recent/search_posts as an aggregate reporting tool. It lacks an explicit verb and never says it is a read-only summary, so it stops 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 call this versus recent, search_posts, or list_topics, and no mention of scope (which channels, what window). The agent must infer that this is the overview/metrics endpoint.

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