Fillin

fillin_answer

Read-onlyIdempotent

Synthesized post-cutoff answer with inline citations.

Requires $0.01–$0.05 USDC (x402) or an Authorization Bearer trial/paid key.
On payment_required, call fillin_probe then fillin_signup (POST /v1/signup)
for a free 20-query trial, or fillin_payment_challenge then retry with
X-Payer-Address, X-Payer-Nonce, X-Payer-Signature (or Bearer).
POST /v1/probe for 1 free taste/IP/day.

Use this when your model is small / cheap / weaker at tool-result
synthesis (Llama, Gemini Flash, Mistral, Nemotron, Qwen). Fillin runs
a server-side LLM pass over the retrieved post-cutoff documents and
returns a 150-250 word answer with [title](url) citations already
embedded — you can quote it directly.

Premium models (Opus, Sonnet, GPT-4o) usually get better results from
`fillin_query` and synthesizing themselves, but this tool works for
any caller. Costs more than fillin_query because of the synthesis pass.

Returns:
    A dict with:
      - answer: the synthesized paragraph (str | None)
      - citations: list of {title, url} extracted from the answer
      - corpus_match: "strong" | "weak" | "none" — quality of retrieval
      - top_score: float — top reranked similarity score
      - model: the synthesizer model used (e.g. claude-haiku-4-5)
      - reason: set when answer is None (e.g. "no_relevant_docs")
      - results: raw post-cutoff documents (same shape as fillin_query)
      - cutoff, query, gap_days: echoes for context

Input Schema

TableJSON Schema

Name	Required	Description
`k`	No	Number of documents to ground the answer in (1-20).
`query`	Yes	Natural-language question, max 512 chars.
`cutoff`	Yes	Training cutoff as ISO-8601 date (e.g. 2026-01-01).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedMay 22, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Despite annotations already providing readOnlyHint, openWorldHint, and idempotentHint, the description goes far beyond by disclosing payment requirements (USDC/x402 or Bearer), rate limits (1 free taste/IP/day), error handling (payment_required), synthesis behavior (150-250 word answers with citations), and the full return structure. This is comprehensive behavioral disclosure that the annotations do not cover.

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

Conciseness4/5

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

The description is well-structured with a clear opening, payment/usage sections, and a return schema. It is fairly long but each sentence adds meaningful information (payment flows, usage scenarios, return fields). It could be slightly tightened, but the density is justified given the tool's complexity.

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

Completeness5/5

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

Given the tool's complexity (payment flows, multiple auth modes, error handling, synthesis behavior), the description is exceptionally complete. It covers prerequisites, exact retry procedures, the intended user profile, and even the return shape beyond the provided output schema. Nothing an agent needs to call this correctly is missing.

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 100% and the input schema already describes each parameter (query, cutoff, k) adequately. The description does not add new parameter-specific meaning beyond what the schema provides; it focuses on tying parameters to the broader synthesis pipeline. Baseline 3 is appropriate since the schema does the heavy lifting.

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

Purpose5/5

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

The description explicitly states a specific verb ('synthesized post-cutoff answer with inline citations') and resource ('retrieved post-cutoff documents'), and distinguishes itself from fillin_query by mentioning it runs a server-side LLM pass. It clearly identifies the tool's unique value proposition.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use ('when your model is small / cheap / weaker at tool-result synthesis') and when-not-to-use ('Premium models usually get better results from fillin_query') guidance, and even names the alternative fillin_query. It also covers payment prerequisites and fallback flows (probe → signup, payment_challenge → retry with headers), leaving no ambiguity.

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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