Skip to main content
Glama

Sciencebase

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".

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds concrete behavioral details: embedding model, cosine similarity, 500-char windows, 200K character cap with truncation and flagging. No contradiction.

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?

Four sentences that efficiently cover purpose, usage, pairing, and technical details. No filler; every sentence adds information.

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 medium-complexity tool with no output schema, the description is complete: it covers purpose, usage, parameters, behavior, and limitations. No gaps remain for an AI agent to misinterpret.

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 good descriptions. The description adds value by explaining that text should be previously fetched, providing example queries, and clarifying the return of passages with offsets. Exceeds baseline 3.

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 it performs semantic search inside a fetched record, distinguishes from siblings by mentioning a pairing with ask_pipeworx_grounded, and provides specific examples of use cases.

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 tells when to use ('when the record is too big to cram into the prompt') and mentions an alternative/pairing tool (ask_pipeworx_grounded).

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

All Sciencebase toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation4/5

Most tools have distinctly described purposes, but there is some overlap among query tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim, which could cause confusion for an agent.

Naming Consistency3/5

Tool names are consistently snake_case but vary in pattern: some are verb_noun (ask_pipeworx, compare_entities), while others are noun_noun (entity_profile, polymarket_arbitrage) or longer phrases (scan_competitor_ai_presence), making the naming system inconsistent.

Tool Count2/5

With 33 tools, the server feels over-scoped. Many tools are niche (e.g., polymarket-specific ones) and the high number exceeds the typical range for a focused server, leading to potential overwhelm.

Completeness3/5

The server covers a broad range of query and monitoring tasks for its domains, but lacks write operations (except memory tools). There are notable gaps like no tool for creating or editing ScienceBase items, which seems incomplete given the server's name.

Resources