Skip to main content
Glama

Tradier

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. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, non-destructive), the description details using BGE-base-en embeddings, cosine similarity over 500-char windows, a 200K char limit with truncation flag, and offsets for verification. No contradiction with annotations.

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?

Single, well-structured paragraph. First sentence front-loads the purpose. Every sentence adds value: use case, pairing, technical details, limits. No fluff.

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?

Without an output schema, the description fully explains return values (top-N passages with offsets and similarity scores) and limitations (200K char cap with truncation flag). Complete for a tool with 3 parameters and no output schema.

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 descriptions for all params. The description adds context by explaining the workflow (pass text you already pulled plus query, get back passages). This reinforces and elaborates on the schema, moving beyond 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 the tool performs semantic search inside a fetched record, with specific examples (SEC 10-K, article). It distinguishes itself from siblings by mentioning how it pairs with ask_pipeworx_grounded, and the purpose is explicit and unique.

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 to use when the record is too large for the prompt, and explains how it saves context. Mentions pairing with a sibling tool, providing clear guidance on when to use.

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

All Tradier 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

A3.7/5.0
Disambiguation2/5

Multiple tools share overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, and the five polymarket_* tools cover heavily overlapping territory. An agent must read lengthy descriptions to distinguish between them, and pairs like validate_claim vs ask_pipeworx_grounded or discover_tools vs suggest_questions have fuzzy boundaries.

Naming Consistency2/5

Naming is wildly inconsistent: verb_noun (compare_entities, discover_tools), bare verbs (forget, remember, subscribe), noun phrases (entity_profile, recent_alerts, pipeworx_feedback), prefixed families (tradier_*, polymarket_*) and suffixed variants (ask_pipeworx_beta, ask_pipeworx_grounded). There is no single predictable convention across the set.

Tool Count2/5

34 tools is far beyond the 15-25 heavy range, and the count is inflated by near-duplicates like ask_pipeworx/ask_pipeworx_beta and five polymarket edge tools. The server mixes several unrelated domains (Tradier quotes/options, Pipeworx research, Polymarket analysis, memory, subscriptions, web utilities), making the scope feel unbounded.

Completeness2/5

For a server named Tradier, having only quote and option-chain endpoints is a significant gap—no historical data, account, positions, or order execution. The Pipeworx/Polymarket side is more complete, but the inclusion of unrelated utilities like generate_llms_txt and scan_dependency means no single domain is fully served.

Resources