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Glama

Cbs Il

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

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds that results include full input schemas with curated examples, ready to call directly. No contradictions 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and usage instruction. It lists many example domains, which is helpful but slightly verbose. Overall well-structured and clear.

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 no output schema, the description adequately explains what is returned (top-N relevant tools with names, descriptions, and schemas). For a discovery tool with simple behavior, this is complete and sufficient.

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 description coverage is 100%, so baseline is 3. The description does not add additional semantics beyond the schema for parameters, but it does explain the overall purpose of the query parameter. The aliases are noted but add minimal value.

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 explicitly states the tool discovers tools based on a natural language description of data or task, listing many domains. It clearly distinguishes from siblings like catalog_browse or search_within by focusing on tool discovery rather than data search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises using this tool when browsing or discovering tools and explicitly says 'Call this FIRST when you have many tools available.' It provides clear context but does not mention when to use alternatives or when not to use it.

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

All Cbs Il toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have genuinely unclear boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, and the polymarket_edges / polymarket_arbitrage / polymarket_kalshi_spread trio all scan for mispricings with overlapping descriptions. The rich usage notes help, but they cannot fully rescue a set where two tools literally do the same thing right now.

Naming Consistency3/5

The majority of tools use readable snake_case, but conventions are mixed: verb-first names (get_index_data, resolve_entity, validate_claim) sit alongside noun-first names (catalog_browse, index_catalog, entity_profile, bet_research), standalone verbs (remember, forget, subscribe), and adjective-led names (recent_alerts, deep_research). The pattern is predictable within clusters but not uniform across the set.

Tool Count2/5

35 tools is well above the comfortable range, and the set spans many unrelated domains—CBS Israel statistics, Pipeworx data routing, Polymarket betting, memory, subscriptions, npm dependency scanning, and AI visibility checks. Even if each tool has a purpose, the surface is bloated and poorly scoped for a server named 'Cbs Il'.

Completeness4/5

For a general data-access gateway, the set covers the major workflows: routing questions, grounded verification, deep multi-source research, entity profiles, comparisons, subscriptions, memory, and tool discovery. Minor gaps exist, such as no direct raw-record fetch without routing and no keyword search over the CBS catalog, but agents can work around these.

Resources