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Glama

flights

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

A4.7/5.0
Behavior5/5

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

Even with readOnlyHint/idempotentHint annotations, the description adds significant behavioral detail: the exact embedding model (BGE-base-en), chunking strategy (500-char overlapping windows), similarity metric (cosine), and a 200K-character cap with truncation-and-flag behavior. This transparency helps an agent predict output quality and avoid size-related surprises.

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?

At roughly 120 words, the description is dense yet every sentence earns its place. It front-loads the core purpose, then covers usage, pairing, and technical constraints in a logical flow, with no redundant or filler language.

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 tool has no output schema, so the description correctly carries the burden of describing the return format — 'top-N passages with character offsets and similarity scores.' Combined with the truncation cap and pairing guidance, the description gives an agent all the contextual information needed to choose and invoke the tool effectively.

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?

The input schema already provides 100% coverage with detailed descriptions for text, query, and limit (including max length, default, and query examples). While the description reinforces roles via phrases like 'text you already pulled' and 'natural-language query,' it introduces no new parameter-level semantics beyond what the schema already contains. It meets the baseline but does not elevate it.

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 opens with the precise verb-resource phrase 'Semantic search INSIDE a fetched record,' immediately distinguishing it from broader search tools. Concrete examples (SEC 10-K, article, long tool result) further anchor its niche, and the contrast with 'too big to cram into the prompt' makes the purpose unmistakable.

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?

It explicitly states the triggering condition — 'Use when the record is too big to cram into the prompt' — and highlights the context-saving benefit. It also names ask_pipeworx_grounded as a complementary sibling and describes how to chain it, which is actionable guidance beyond a generic list of 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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research blur the query/research boundary, and the polymarket_* family plus bet_research all target prediction-market analysis. An agent would frequently struggle to pick the correct tool among these near-duplicates.

Naming Consistency3/5

All names are snake_case, but the verb/noun style is inconsistent: get_* for flight lookups, ask_* for queries, noun-style names like entity_profile and bet_research, and the polymarket_* prefix group. Some subgroups are internally consistent, but there is no single predictable pattern across the set.

Tool Count2/5

36 tools is far too many for a server named 'flights' — only 5 tools actually relate to aviation, while the rest span prediction markets, SEC/FDA data, npm packages, memory, and feedback. Even viewed as a general data platform, the count is heavy and the scope is unfocused.

Completeness2/5

For a flights server, the surface is severely incomplete: no scheduled flight status, delays, cancellations, or airport schedules — only live ADS-B snapshots. For the broader data-research domain the tools imply, coverage is better but still scattered, with no coherent lifecycle and several one-off utilities that don't connect to the rest.

Resources