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Glama

Endoflife

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

A4.5/5.0
Behavior4/5

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

Annotations already provide idempotentHint=true. Description adds context: scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous sessions. 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 two sentences with additional context. Front-loaded with purpose, every sentence earns its place. No redundant information.

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 2 parameters, no output schema, and annotations present, the description covers purpose, usage, parameter semantics, and runtime behavior (24h expiry, persistence). Fully adequate.

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%. Description adds examples for key ('subject_property', 'target_ticker') and value ('findings, addresses, preferences'), clarifying usage beyond schema descriptions.

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?

Description clearly states the tool saves data for reuse across conversations/sessions, and gives concrete examples like resolved ticker, target address. It distinguishes from sibling tools 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 Guidelines4/5

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

Description specifies when to use ('when you discover something worth carrying forward') and mentions pairing with recall/forget. Could improve by explicitly stating alternatives or when not to use.

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

All Endoflife toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route research questions; polymarket_edges, polymarket_arbitrage, and bet_research all scan prediction markets for opportunities; ai_visibility_check and scan_competitor_ai_presence overlap. Long descriptions differentiate them, but an agent selecting quickly could easily pick the wrong one.

Naming Consistency2/5

Naming mixes brand prefixes (ask_pipeworx, pipeworx_feedback), domain prefixes (polymarket_arbitrage), bare verbs (remember, forget, recall, subscribe), and noun-ish phrases (entity_profile, resolve_entity). There is no consistent verb_noun or resource-based pattern across the set.

Tool Count2/5

34 tools is heavy, and for a server named Endoflife only 3 tools actually relate to product lifecycle dates. The rest are unrelated Pipeworx data, Polymarket, memory, and utility tools, making the count feel bloated and mismatched to the apparent purpose.

Completeness3/5

The end-of-life domain itself is reasonably complete: list_products, get_product, and get_cycle cover lookup needs. However, the broader tool surface is a patchwork of unrelated subsystems—data research, prediction markets, memory, AI visibility, subscriptions—with no single cohesive domain that completeness can be judged against.

Resources