Wikipedia Trends MCP

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

With no annotations, the description carries the burden and does meaningful work: it discloses the returned value scale (0-100 plus absolute volume when available), the rate-limit/quota failure behavior and prescribed user messaging, and a semantic caveat about app sources. It does not cover auth/permission requirements, but for a read-style analytics call that is a minor gap.

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?

Six short sentences, front-loaded with purpose and output semantics, followed by sibling disambiguation and error behavior; every sentence carries distinct information with no 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?

No output schema exists, so the description's explanation of the 0-100 scale and absolute volume is necessary and present; combined with full parameter documentation and quota behavior, an agent has everything needed to call it 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 description coverage is 100%, so all three parameters (including the full preset list and source/keyword formats) are already documented in structured data. The description only restates the window preset idea and the Android bundle ID rule without adding format or syntax detail beyond the schema, so baseline 3 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?

States a specific verb and resource ('Point-to-point growth for a keyword on one or more sources') and explicitly contrasts itself against the sibling get_time_series for growth questions, so an agent can pick it without opening the schema.

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 says when to prefer this over get_time_series, clarifies that app downloads/rankings sources are not the live boards from get_top_trends, and specifies what to do on rate limit or quota exhaustion.

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 well: it discloses the output scale (0-100 + volume), and that rate limiting/quota exhaustion can occur along with the correct user-facing message. It stops short of describing auth requirements, latency, or truncation of long historical ranges.

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 tight sentences, each doing distinct work (what it returns, positive use case, two negative routings, error handling). No filler and the return format is front-loaded.

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, and the description compensates by stating the value range and the optional volume field. Combined with explicit sibling routing and quota guidance, an agent has enough to call it correctly; only deeper return-shape detail (e.g. date granularity) is absent.

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

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

Schema coverage is 100% and the schema already spells out valid sources and per-source keyword formats, so the description adds little beyond 'one keyword and one source'. Baseline 3 applies when the schema does the heavy lifting.

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

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

States a specific verb+resource ('Full historical series for one keyword and one source') and quantifies the return ('0-100 values, plus volume when available'). It also explicitly distinguishes itself from both siblings, so an agent can route without opening any schema.

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 explicit when-to-use ('charting or custom math'), when-not ('Not for live "what's trending now" boards'), and names the alternative for each case (get_top_trends, get_growth). It even covers the error path by telling the agent what to say when rate limited or out of quota.

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 that a missing board is fetched live by the handler, that rate limiting/quota exhaustion should be surfaced as a plan limit to the user, and that default sort is current rank. It stops short of describing auth/permission requirements or response shape, which keeps it from a 5.

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-loaded with purpose, then usage rules and exclusions. Each sentence carries information, though the category enumeration and country-type list overlap heavily with the schema, adding mild 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 7-parameter, no-annotation, no-output-schema live-feed tool, the description covers routing, fallback behavior, and error/quota handling adequately. Return-value details are left to the schema's parameter text, which 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?

Schema description coverage is 100%, so the schema already documents sort, type, limit, offset, window, country, and category in detail. The description largely restates the schema's category rule and sort/window semantics, adding little syntax or format detail beyond it; baseline 3 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?

Opens with a specific verb+resource+scope: 'Live top-trending board for exactly one feed type.' It distinguishes itself from siblings by explicitly naming get_growth and get_time_series as the tools for app history. An agent can route correctly without opening any schema.

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 explicit when-to-use rules (always pass category for five named feed types), explicit exclusions ('Do not use get_time_series for live boards'), and names the alternatives for app history. The rate-limit/quota behavior instruction is also given.

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