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Glama

Septa

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already provide readOnly, idempotent, non-destructive hints. The description adds valuable behavioral context: returns top-N tools with schemas and examples, ready to call directly, no second lookup needed. No contradictions.

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?

Description is efficiently front-loaded with purpose, then usage, then return format. Every sentence provides unique value; no wasted words.

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?

Given the low complexity and high schema coverage, the description fully explains input (natural language query), output (list with schemas), and usage context. No gaps.

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?

Schema coverage is 100%, so baseline is 3. The description adds examples and clarifies it accepts natural language, but does not significantly add parameter meaning beyond the schema definitions.

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 discovers tools by task description, listing many domains. It explicitly distinguishes from sibling tools by advising to call this first when many tools exist.

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: 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set.' Implicitly tells when not to use (when you already know the tool).

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

All Septa toolsInspect
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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap among the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and among the Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread). However, detailed descriptions help differentiate them.

Naming Consistency3/5

Tool names mix conventions: snake_case is predominant, but there is no consistent verb_noun pattern. Some tools have 'septa_' prefix, but the majority do not follow a predictable structure. Naming is inconsistent across the set, though subgroups have some consistency.

Tool Count3/5

With 36 tools, the count is high. The server name 'Septa' suggests a focused transit server, but the tool set covers transit, data access, and prediction markets, making it overly broad. The number is borderline appropriate for a general-purpose server but misaligned with the name.

Completeness4/5

The transit domain is well-covered with alerts, positions, schedules, and train views. The data access and prediction market tools also cover their domains comprehensively. Minor gaps exist (e.g., no dedicated weather tool), but overall the tool set provides a wide range of capabilities.

Resources