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Glama

Sec Regulations

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

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?

Beyond annotations (idempotentHint=true, destructiveHint=false), the description adds key behavioral details: key-value pair storage, scoping by identifier, persistence differences between authenticated and anonymous sessions, and pairing with recall/forget. 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?

Four sentences, front-loaded with the primary action, no wasted words. Every sentence adds value: purpose, use case, mechanism, and context.

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 simplicity of the tool (two simple string params, no output schema), the description covers all essential aspects: what it does, when to use, how it works (scope, persistence), and how it relates to siblings. No gaps.

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 both parameters. The description adds context with example keys (subject_property, target_ticker, user_preference) and value types (findings, addresses, preferences, notes), enriching 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 uses specific verbs ('Save', 'store') and resources ('data the agent will need to reuse later'), clearly distinguishing it from sibling tools like 'recall' and 'forget'. Examples of use cases (ticker, address, preference) further clarify the purpose.

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?

Explicitly states when to use ('when you discover something worth carrying forward') and provides context about persistence differences. Although it doesn't explicitly list when not to use, the positive guidance is strong and alternatives (recall, forget) are mentioned.

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

All Sec Regulations toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools have nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, polymarket_arbitrage and polymarket_edges both scan for betting opportunities, and ai_visibility_check overlaps with scan_competitor_ai_presence. The long descriptions help, but an agent must read carefully to distinguish the meta-routers (ask_pipeworx, deep_research, discover_tools, suggest_questions) from each other.

Naming Consistency4/5

The overwhelming majority use lowercase snake_case with a verb-first or domain-prefixed noun pattern (ask_pipeworx, sec_regulations_search, polymarket_edges). Minor deviations like entity_profile, recent_alerts, and ai_visibility_check are noun-first, and sec_regulation vs sec_regulations_search is slightly inconsistent, but the overall style is predictable.

Tool Count2/5

At 33 tools this exceeds the 25-tool threshold for 'too many', and most are unrelated to the server's stated name 'Sec Regulations'—only sec_regulation and sec_regulations_search fit that label. While each tool has a defined purpose, the set feels like several distinct servers (research, betting, memory, subscriptions) crammed into one.

Completeness3/5

For a general data-research platform, the surface is broad: Q&A routers, entity resolution/profiles, comparisons, validation, memory, subscriptions, and domain-specific tools. But the SEC-regulation focus implied by the server name is severely under-served, and there's no direct tool to fetch a raw document by citation/URI outside of the ask_pipeworx router. The Polymarket cluster is over-built relative to other data domains.

Resources