fetchRetrieve the full text of one document by the id returned from search.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Document id from search results |
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| url | Yes | ||
| text | Yes | ||
| title | Yes | ||
| metadata | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and side-effect behavior. The description adds that the tool returns full text and expects a search-derived id, but it does not disclose additional behavior beyond what structured annotations already provide.
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 one concise, front-loaded sentence. It communicates the action, the resource, and the input source without any 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 single-parameter tool with an output schema and annotations covering safety and idempotency, the description is nearly complete. The only notable gap is the lack of differentiation from the sibling 'get_article', which could create mild selection ambiguity.
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?
Input schema coverage is 100%, with the 'id' parameter already described as 'Document id from search results'. The description similarly mentions the id comes from search, but adds no further semantic detail beyond the schema, so the baseline score of 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?
The description states a specific verb ('Retrieve'), resource ('full text of one document'), and input ('id returned from search'). It clearly distinguishes from search tools, but it does not differentiate from the sibling 'get_article', which may also fetch a document by id.
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 correct usage sequence: run 'search' first, then use the returned id to fetch the document. However, it does not explicitly state when to choose this tool over the sibling 'get_article' or 'search_articles', so guidance on alternatives is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Search and search_articles overlap significantly, both retrieving article matches with slightly different result formats. Fetch and get_article also both return full article text but are distinguished by id versus slug. Descriptions help resolve these overlaps, but an agent could still pick the wrong tool without close attention.
Most tools follow a snake_case verb_noun pattern like get_article and search_articles, but fetch and search are bare verbs, and popular_content is a noun phrase with no verb. The pattern is readable but not fully consistent.
Six tools is a well-scoped set for a publication content gateway: search, retrieval by two identifier types, recent listing, popular content, and article search. Each tool serves a distinct practical need without bloat.
The core content access workflows are covered: search, retrieve full text by search id, retrieve by slug, list recent, and list popular. Minor gaps exist, such as no pagination or filtering, and search_articles results don't explicitly feed into get_article or fetch, but agents can typically work around these.