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Glama

Mbta

by io.github.pipeworx-io

Remember

remember
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, no destructiveness), description details persistence (24 hours for anonymous, persistent for authenticated), scoping by identifier, and key-value storage, adding significant behavioral context.

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?

Three concise sentences, front-loaded with purpose, each sentence adding distinct value (purpose, usage, behavior). 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?

For a simple 2-param tool with no output schema, description fully covers what, when, and how, including behavioral nuances, making it complete.

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?

With 100% schema coverage, description adds naming conventions and value semantics beyond schema, enhancing clarity without redundancy.

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 verb 'save' and resource 'data to reuse later', and distinguishes from sibling tools recall and forget by pairing them explicitly.

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?

Description provides explicit context for when to use ('when you discover something worth carrying forward') but does not state when not to use, though the use case is well-defined.

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

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TDQS

A3.9/5.0
Disambiguation2/5

ask_pipeworx_beta is explicitly stated to be currently identical to ask_pipeworx, which is a direct duplication. The polymarket cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes around finding and validating betting edges, and the ask_pipeworx / ask_pipeworx_grounded / deep_research / validate_claim tools all handle natural-language 'look up X' queries, making misselection likely without reading lengthy descriptions.

Naming Consistency4/5

snake_case is uniform and helpful prefixes (mbta_, polymarket_, pipeworx_, ask_pipeworx) create recognizable families. However, verb style is inconsistent — imperative verbs like ask/compare/discover/validate mix with noun-first names like bet_research, entity_profile, and search_within, and the memory trio (remember/recall/forget) doesn't share a common prefix.

Tool Count2/5

At 35 tools this exceeds the comfortable range, and the count is inflated by near-duplicates (ask_pipeworx_beta) and a dense 6-tool polymarket family. The server also mixes unrelated domains — only 4 of 35 tools are MBTA transit tools while the rest are Pipeworx data research, prediction markets, memory, and subscriptions — making it a kitchen sink rather than a well-scoped set.

Completeness4/5

The Pipeworx research surface is thorough: query, grounded verification, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and subscriptions are all covered with few dead ends. Minor gaps exist — the MBTA portion lacks schedule/line-detail tools beyond departures and alerts, and the AI-visibility feature feels bolted on without deeper integration.

Resources