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Glama

The Graph

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

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

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds substantial context: embedding model (BGE-base-en), similarity metric (cosine), windowing (500-char overlapping windows), and limits (200K chars, truncation flag). This goes well beyond what annotations provide.

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 front-loaded with the main purpose and includes necessary details in a logical flow. It is slightly long but every sentence adds value, making it efficient overall.

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?

The description covers return values ('passages with character offsets and similarity scores'), limitations (200K char cap with truncation), and integration with sibling tools. No output schema exists, so this completeness is essential and well-handled.

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

Parameters4/5

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

Schema coverage is 100% with parameter descriptions. The description adds meaning by providing concrete examples (e.g., 'SEC 10-K body', 'supply-chain risk') and clarifies the purpose of the 'text' and 'query' parameters. The 'limit' parameter is implied but not explicitly detailed in the description beyond 'top-N'.

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 clearly states the action ('semantic search') and the resource ('inside a fetched record'), with a specific verb and resource. It distinguishes from sibling tools like ask_pipeworx_grounded by explaining the focused use case.

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?

Explicitly says 'Use when the record is too big to cram into the prompt' and suggests pairing with ask_pipeworx_grounded for grounding, providing clear context for when to use this tool over alternatives.

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

A3.8/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and deep_research/ask_pipeworx plus the many polymarket_* tools have adjacent intents that are easy to confuse. scan_competitor_ai_presence also directly wraps ai_visibility_check, so an agent can easily select the wrong tool for the same task.

Naming Consistency3/5

Names are consistently snake_case, but the convention is mixed: some are verb-first (query_subgraph, introspect_schema, validate_claim), some are noun-first (entity_profile, polymarket_edges, pipeworx_trending), and some are bare verbs (remember, recall, forget). The repeated prefixes help, but there is no single predictable naming pattern across the set.

Tool Count2/5

With 33 tools, this exceeds a reasonable single-server footprint, and the scope sprawls across data routing, prediction markets, AI visibility, npm dependencies, llms.txt generation, memory, and subscriptions. Many tools feel like separate mini-applications rather than one coherent toolkit for The Graph.

Completeness3/5

The Pipeworx data-research and prediction-market side is well covered with lookup, grounded answers, research, comparison, profiles, claims, subscriptions, and memory. However, the server's apparent namesake, The Graph, is thin: only query_subgraph and introspect_schema exist, with no subgraph discovery, status, or management tools.

Resources