Skip to main content
Glama
SK-DEV-AI

codesearch MCP Server

by SK-DEV-AI

enrich

Fetches full page content for search results, deduplicates entries, and reranks them by relevance to your original query.

Instructions

Fetch full content for a list of search results, deduplicate, and rerank by relevance. Give it results from any search tool plus the original query. e.g. enrich(query='rust async', results=[{'url':'...','title':'...'}])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNoMax enriched results to return
resultsYesList of result objects to enrich (url, title, text/snippet, source)
include_htmlNoInclude raw HTML in content (default strips to text)
max_fetch_sizeNoMax results to fetch content for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries full burden and does disclose the core behavior: it fetches page content, deduplicates, and reranks by relevance. It does not describe failure handling for unreachable URLs, timeouts, fetch volume limits, or what the returned structure looks like, which matters for a network-fetching transform.

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

Conciseness5/5

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

Three compact sentences: purpose first, input contract second, example last. Every element is front-loaded and earns its place, and the inline example resolves ambiguity about result shape without padding.

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

Completeness3/5

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

The description covers the input contract well enough to call the tool, but with no output schema it should explain what enrichment returns (e.g., full text field per result, ordering guarantees), and it leaves two parameters' relationship and fetch failure behavior unaddressed. Adequate but with clear gaps for a 5-parameter transform tool.

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

Parameters3/5

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

Schema coverage is 80%, so baseline is 3. The description reinforces that 'results' should come from a search tool and that 'query' is the original query, and the example clarifies the minimal result shape (url, title), but it does not explain the distinction between top_k and max_fetch_size or the meaning of include_html beyond what the schema already says.

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

Purpose4/5

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

The description states a specific pipeline verb set (fetch, deduplicate, rerank) over a specific resource (a list of search results), which is far clearer than the vague name 'enrich'. It implicitly distinguishes itself from the search_* siblings by being a post-processing step, but never names a sibling or explicitly contrasts with them.

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

Usage Guidelines4/5

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

It gives a clear usage context: pass results from any search tool plus the original query, which effectively tells the agent this runs after a search. There are no explicit when-not-to-use conditions or named alternatives, but the ordering dependency is clear from the phrasing and example.

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