Search Fragments
Server Details
Fragment synthesis for agents. Resolves half-remembered queries; never returns a confident-wrong.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.9/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion. The tool's purpose is uniquely defined and distinct from any other tool in the server.
With a single tool, naming consistency is perfect. The name 'resolve_fragment' clearly conveys its function.
The server has one tool, which is slightly below the typical range of 3-15 for a well-scoped server, but the tool is highly specialized and earns its place for the specific niche of resolving vague queries.
The tool covers the entire domain of resolving half-remembered queries, providing multiple result types and clearly stating its limitations. There are no obvious gaps for its stated purpose.
Available Tools
1 toolresolve_fragmentARead-onlyInspect
Synthesise an answer to a half-remembered or hard-to-place query — one carrying only indirect clues, partial descriptions, remembered attributes, or relationships, the kind that normally takes several searches and dead-ends to run down. Returns one of three honest results: a named candidate with a confidence level, a ranked shortlist of sources to piece together, or an explicit 'not resolvable from text.' Never asserts a confident answer — every result is decide-by-eye. Not for direct or single-fact lookups; a normal search is faster for those.
Examples:
a musician who became famous largely for stopping performing
somebody who photographed the same view every day until the changes became the artwork
a song everybody knew but nobody could identify
the company that bought Instagram before it was big
a novel where the footnotes slowly become the real story
Not for:
what is the capital of France
who directed Jaws
name of french artist cubist painting 1948
| Name | Required | Description | Default |
|---|---|---|---|
| fragment | Yes | The half-remembered or fragment-shaped query, in the user's own words. Should be at least 5 words and describe the thing being recalled by premise, plot, relationship, or context. |
Output Schema
| Name | Required | Description |
|---|---|---|
| pool | Yes | Ranked web results. Always DECIDE-BY-EYE — human must confirm before any graph write. |
| outcome | Yes | 'resolved' = strong candidate identified (semantic pass returned high/medium confidence title). 'ranked_stalls' = web results returned but no confident semantic identification — pool for human review. 'no_resolution' = not an SF question, or pipeline produced an empty pool. |
| candidate | Yes | Top candidate when outcome=resolved; null otherwise. DECIDE-BY-EYE — human must confirm. |
| reject_reason | Yes | Why the fragment was rejected; non-null only when is_sf_question=false. |
| is_sf_question | Yes | true if the fragment is a valid memory-recall query; false if it was rejected as a direct lookup or research prompt. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds significant behavioral context beyond annotations: it discloses three types of results, confidence levels, that it never asserts confident answers, and that results are 'decide-by-eye.' Annotations already indicate readOnlyHint and openWorldHint, but description expands on them without contradiction.
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?
Well-structured with sections, examples, and anti-examples. Every sentence adds value, though slightly verbose. Front-loaded with main purpose.
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 the tool's complexity (handling vague queries), the description fully covers purpose, usage, behavioral nuances, and parameter semantics. Output schema existence noted, but description stands alone as 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?
Schema covers 100% of parameters, but description adds meaning: it explains the 'fragment' parameter should be in user's own words, at least 5 words, and describe the thing being recalled by premise, plot, relationship, or context. This adds significant value beyond schema constraints.
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 tool is for synthesizing answers to half-remembered or hard-to-place queries, distinguishing it from direct fact lookups. It uses specific verbs and resources like 'synthesise an answer' and 'returns one of three honest results.'
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 provides when to use (fragment queries) and when not to (direct fact lookups), with concrete examples and counterexamples. It also notes alternatives like 'a normal search is faster for those.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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