memecub

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

No annotations are provided, so the description carries the full behavioral burden. 'Get' implies a safe read, and listing the returned fields (license, source, author, credit line, tags) adds real value absent an output schema, but it says nothing about not-found handling, id format, or auth requirements.

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 and scope, with the field list appended; no filler or redundancy.

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

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

For a one-parameter read tool with no annotations and no output schema, the description usefully enumerates the returned fields, but leaves the id format and failure behavior unexplained, leaving gaps an agent must guess at.

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 'id' parameter has 0% schema description coverage, so the description is the only source of meaning — it says 'by id' but never specifies the id's format or provenance. It identifies what the parameter selects without decoding its syntax.

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 (get) and resource (one image by id) and enumerates the fields returned, which separates it from search_memes. It does not name a sibling for differentiation, but the 'one image by id' scope 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?

Usage is only implied: 'by id' signals you must already have an identifier, positioning it opposite search_memes, but no when-to-use or when-not-to-use guidance is stated explicitly.

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 does disclose the payload shape (categories paired with image counts), which implies a read-only enumeration, but says nothing about ordering, pagination, or completeness of the category set. For a zero-parameter read tool the risk is low, so this is adequate rather than negligent.

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 short fragment with zero filler and the key content (image counts) front-loaded. It is efficient, though arguably terse to the point of under-specification rather than optimal concision.

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 annotations, the description must carry the return-shape burden, and it only partially does so: it names categories and image counts but not the fields of a category object or the result ordering. For a trivial parameterless list tool this is minimally sufficient, not 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 tool takes no parameters, so there is nothing for the description to disambiguate and the baseline is 4. The empty schema is consistent with the parameterless listing the description implies.

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 phrase identifies the resource (library categories) and what is attached to each (image counts), so an agent can tell it apart from get_meme, search_memes, and match_article. However, it is a noun fragment with no explicit verb, so it only implies the listing action rather than stating it.

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 use this tool versus the sibling retrieval tools, nor any precondition or ordering guidance. The listing intent is inferable from the name alone, but the description contributes nothing.

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 and does well: it discloses the API key requirement, the 20-credit-per-image cost, and the shape of the return value. It stops short of stating whether the operation mutates anything server-side or how failures are reported, which matters for a paid call.

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

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

Three sentences, each earning its place: capability, then return shape, then cost/alternative. The most decision-relevant constraint (cheaper alternative) is placed where it will be read, 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?

The detailed return-value description compensates well for the absent output schema, and the auth/cost notes cover the operational side. The remaining gap is the licensing filter parameters, which are the main thing an agent could get wrong when calling this 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 only 40%, so the description needed to compensate and does not. Three filters (no_credit, commercial_only, include_profanity) and the count/alternatives relationship go entirely unexplained, leaving the licensing semantics of no_credit vs commercial_only — which overlap confusingly — undocumented 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?

Specific verb and resource ('Reads an article') plus an unusually precise statement of what comes back: matched spots, a chosen image with alternatives, and the article re-emitted as Markdown with images inserted. It is clearly distinguishable from search_memes, which returns candidates rather than placements.

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 names the alternative (calling search_memes per spot) and the condition that selects it: if the agent can read the article itself, the per-spot route is far cheaper. The cost differential (20 credits vs 1 credit) makes the routing decision concrete rather than advisory.

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

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

With no annotations, the description carries the full burden and does real work: it discloses that every result reports commercial-use clearance, that results include a ready-made Markdown snippet with any required credit line, and that authenticated callers additionally receive their private library. It does not cover pagination, rate limits, or empty-result behavior, but the output-shape and auth-dependent behavior disclosures are substantive.

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 dense sentences, front-loaded with the corpus scope before moving to query phrasing and output guarantees. Every clause carries information, though the parenthetical licensing details make it slightly heavy to scan.

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

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

For an 8-parameter search tool with no output schema and no annotations, the description covers the corpus, query strategy, licensing metadata, credit-line behavior, and auth-gated private results. It omits return ordering/pagination and the meaning of kind values like 'upload' (left to the schema), leaving only minor gaps.

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

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

Schema coverage is already 88%, so the baseline is 3, but the description adds genuine meaning: it explains the commercial_use distinction that drives commercial_only and no_credit, clarifies the private-library behavior behind private_only, and adds query semantics (language determines result language) that the schema does not encode.

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 (search) and a precisely bounded corpus: web-collected Chinese 表情包/斗图 and English reaction memes plus openly licensed emoji, art and photos. It also states the licensing split (commercial_use=false vs true) and language-dependent result behavior, which cleanly separates it from a direct-fetch sibling like get_meme.

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 actionable usage guidance ('search for the reaction you want in 1-3 words') and explains that Chinese queries surface Chinese 表情包 while English queries surface English memes, which shapes how an agent should phrase a call. It stops short of naming alternatives or stating when not to use this tool versus get_meme or list_categories.

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