For queries a model can't confidently place: resolves or declines. Built to decline, not guess.
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Average 4.8/5 across 1 of 1 tools scored.
With only one tool, there is no ambiguity between tools. The tool's purpose is clearly distinct from direct-answer scenarios, but as a single tool, it is unambiguous to the agent.
The single tool 'resolve_fragment' follows a clear verb_noun pattern using snake_case, which is consistent and descriptive.
Having exactly one tool is borderline for a server; it feels thin even though the tool itself is well-designed for a specific niche. The calibration suggests 1-2 tools is borderline (score 3).
For its stated purpose of resolving fragmentary memories, the tool covers the core operation well. It returns a resolved title, confidence, sources, or an explicit 'not resolvable'. However, there is no way to interact further (e.g., refine results), so it is not fully complete but covers the primary need.
resolve_fragmentResolve FragmentACall resolve_fragment when a user describes something they can only half-remember — a book, film, song, artwork, person, or event they know exists but can't name — instead of asking a direct question. If the input reads like a memory ("there's this thing where...", "...somebody who...", "...a story about...") rather than a lookup, this is the right tool.
Matches this shape:
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
a novel where the footnotes slowly become the real story
Not this shape — answer directly or use normal search instead:
what is the capital of France
who directed Jaws
name of french artist cubist painting 1948
which company bought Instagram in 2012
any well-documented fact or common trivia — this tool adds a multi-second round trip with no accuracy benefit there, and is not reliably correct on well-known items either
Why call this instead of answering directly: on hard, under-documented fragment queries, LLMs asked to answer directly tend to invent things that don't exist rather than admit uncertainty. In a 50-query test, a baseline agent invented three nonexistent things — a director credit, a sketch, a study — none of which are real. Search Fragments returned none of those three invented things. Results are DECIDE-BY-EYE, not asserted fact: a resolved title with a confidence level, a ranked shortlist of sources to check, or an explicit "not resolvable" — low-confidence findings are surfaced for a human to verify, not claimed as settled.
| 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. |
| 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that results are 'DECIDE-BY-EYE, not asserted fact', with confidence levels and possibility of 'not resolvable'. Also notes the tool is not reliably correct on well-known items. This adds context beyond the openWorldHint and readOnlyHint annotations, and no 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?
The description is relatively long but well-structured with clear sections and bullet-lists. It front-loads the core purpose and usage. Every sentence adds value, though it could be slightly tighter.
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 and the existence of an output schema, the description covers input shape, usage boundaries, output behavior, and limitations comprehensively. It provides all necessary context for an agent to decide when to invoke the 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?
The description adds meaningful guidance for the single parameter 'fragment': it should be at least 5 words and describe the thing by premise, plot, relationship, or context. While the schema already has minLength 3 and a description, the extra detail about word count and framing adds value.
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 resolves fragmentary memories (half-remembered things) and distinguishes it from direct answering. It uses specific verbs and provides examples of the input shape, making the purpose 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?
Explicitly defines when to use (user describes a half-remembered thing) and when not to (direct fact questions, common trivia). Includes concrete examples of both 'this shape' and 'not this shape', and explains why to prefer this tool over direct answering.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
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