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Agent Rescue

by io.github.santismm

Fetch a URL as structured JSON

fetch_structured

Fetch a public URL and return its content as JSON validated against the schema you pass. Use it when you need data from a page and cannot parse it reliably yourself. Do not use it for URLs requiring authentication or a session, for internal networks, or when you already have the content — extract it yourself in that case. extracted_by tells you where the answer came from: structured_data when the page already published it (JSON-LD, OpenGraph, or a JSON body — exact and free) or model when it had to be extracted from the text. If structured extraction is unavailable, the response carries degraded: true, data: null and the page text for you to parse: check degraded before reading data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic http(s) URL.
schemaYesJSON Schema of the object you want back. Missing fields come back as null.
instructionsNoOptional. What exactly to look for if the page is ambiguous.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are provided, so the description fully owns behavioral disclosure. It explains extraction modes (`structured_data` vs `model`), the degraded mode with `degraded: true`, `data: null`, and fallback `text`, and how to handle missing structured extraction. This is rich, honest behavior documentation.

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?

The description is dense but every sentence earns its place: purpose, usage boundaries, extraction modes, and degraded handling. It is organized from high-level action to detailed response interpretation, making it scannable and appropriately sized for the behavioral 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?

With no output schema and no annotations, the description fully explains expected response fields (`extracted_by`, `degraded`, `data`, `text`) and instructs the agent to check `degraded` before reading `data`. It covers the core functionality, failure modes, and usage exclusions, making the tool fully comprehensible.

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 description coverage is 100% for all three parameters, so the baseline is 3. The description adds minimal context beyond the schema (e.g., 'validated against the schema' reinforces the schema's purpose) but does not meaningfully extend parameter semantics.

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?

States clearly that it fetches a public URL and returns content as JSON validated against a provided schema. The verb 'Fetch' and resource 'URL' are specific, and the exclusion of auth/internal/known content differentiates it from potential alternatives.

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?

Provides explicit when-to-use ('when you need data from a page and cannot parse it reliably yourself') and when-not-to-use (auth, internal networks, already-have-content). It further instructs on interpreting `extracted_by` to know the extraction source, giving clear decision guidance.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a completely distinct concern: atomic claim-checking, structured URL fetching, and capability gap reporting. There is no overlap in purpose or use case.

Naming Consistency4/5

All names are snake_case and start with a verb, but 'check_and_claim' uses a conjunction and compound verb while the others are simple verb_noun. Minor deviation from a strict pattern.

Tool Count4/5

Three tools is small but fits the focused 'rescue' purpose: atomic claim, structured fetch, and capability request. Each earns its place; slightly thin but not deficient.

Completeness4/5

The surface covers key agent rescue needs: idempotency, reliable external data retrieval, and signaling missing capabilities. Minor gaps exist (e.g., no tool for local state inspection) but core workflows are supported.

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