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Glama

Oeis

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

A5/5.0
Behavior5/5

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

Adds storage behavior beyond annotations: persistent memory for authenticated users, 24-hour retention for anonymous. 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?

Well-structured single paragraph: purpose first, then usage, then details. Every sentence adds value; 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?

Given simple tool with 2 params and no output schema, description covers all needed aspects: purpose, usage, behavior, parameter semantics, and lifecycle.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds value with example keys and explains the purpose of storing any text. Clearly maps parameters to use cases.

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 saves data for reuse across sessions, with specific verb and resource (save data). It distinguishes from siblings by mentioning recall and forget.

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?

Explicit when-to-use guidance with examples of what to save (resolved ticker, target address, etc.). Notes alternatives (recall, forget) and context (authenticated vs anonymous sessions).

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

All Oeis toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap, especially among ask_pipeworx variants (ask_pipeworx, ask_pipeworx_grounded, ask_pipeworx_beta) and prediction market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research). While descriptions help distinguish them, the boundaries are somewhat fuzzy.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use verb_noun (search_sequence, compare_entities), others use noun_verb (entity_profile, recent_changes), and some have prefixes (pipeworx_feedback, pipeworx_trending) while others do not (remember, forget). There is no uniform pattern, which reduces predictability.

Tool Count2/5

The server name 'Oeis' suggests a focus on integer sequences, but only 2 of 33 tools are OEIS-related. The remaining tools cover a vast array of unrelated domains (data retrieval, prediction markets, memory, subscriptions). This mismatch between name and scope makes the tool count feel excessive and poorly scoped.

Completeness4/5

Within the broad domains covered, the tool surface is quite complete. For memory, there are remember/recall/forget; for subscriptions, subscribe/unsubscribe/list_subscriptions/recent_alerts; for data retrieval, multiple entry points. Minor gaps exist, such as lack of OEIS sequence contribution tools, but overall the set is well-rounded.

Resources