Skip to main content
Glama

ingest

Add a URL to the knowledge base to enable offline semantic search. Fetches the page with sitemap discovery, indexes it in the background, and returns immediately.

Instructions

Use when you have a URL to add to the knowledge base — a doc, an article, a spec.

remember = a specific durable fact, ingest = a URL, learn = a distilled lesson that gets retrieval preference.

Fetches a URL with smart sitemap discovery and single-page fallback. For local files and directories, use register_directory + sync_all_registrations — the daemon owns the filesystem, so there is no in-process file loader here.

Returns immediately — the daemon indexes in the background.

Args: source: HTTP(S) URL to ingest. overwrite: If true, replace existing data. collection: Collection name. Auto-derived if empty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYes
overwriteNo
collectionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. First observedv2.1.0

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that 'Returns immediately — the daemon indexes in the background', mentions 'smart sitemap discovery and single-page fallback', and explains the filesystem ownership. However, it doesn't discuss failure handling or error cases, which is a minor gap given the richness of other disclosed traits.

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 but slightly verbose. It opens with purpose, then usage differentiation, then behavioral details, and a clear 'Args:' section. Every sentence has value, though the sibling differentiation paragraph could be tightened. Overall it's appropriately sized and front-loaded.

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?

For a 3-parameter tool with no annotations and an output schema, the description provides comprehensive context: purpose, usage, alternative routing, parameter semantics, and async behavior. Nothing critical is missing for an agent to call it correctly, making it highly complete.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It does so fully by explaining each parameter: source is an 'HTTP(S) URL', overwrite 'replace existing data', collection 'Auto-derived if empty'. This adds crucial meaning beyond the bare schema, leaving no parameter vague.

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?

Description clearly states the tool's purpose: 'Use when you have a URL to add to the knowledge base' and explicitly distinguishes it from siblings via 'remember = a specific durable fact, ingest = a URL, learn = a distilled lesson'. It names the verb and resource precisely, leaving no ambiguity about its function.

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 guidance on when to use this tool versus alternatives: 'remember = a specific durable fact, ingest = a URL, learn = a distilled lesson' and for local files 'use register_directory + sync_all_registrations'. This clearly routes the agent to the correct sibling, leaving nothing to inference.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/punt-labs/quarry'

If you have feedback or need assistance with the MCP directory API, please join our Discord server