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Glama

Tavily

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

A4.3/5.0
Behavior4/5

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

Annotations show this is a write (readOnly=false), idempotent, non-destructive operation. The description adds useful context: persistence depends on auth (authenticated = persistent; anonymous = 24h) and values are scoped by identifier. It does not explicitly state whether re-saving the same key overwrites the existing value, but overall it exceeds the annotation baseline.

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?

The description is four sentences, front-loaded with the main action, and each sentence contributes: what it does, when to use it, storage format/scope, persistence, and related tools. No unnecessary filler.

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-parameter tool with no output schema, this description is complete: it explains the tool's behavior, gives use cases, describes memory scope and retention, and points to related tools (recall, forget). No meaningful gaps remain.

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?

The schema already fully documents both parameters (key and value) with examples (100% coverage). The description only reinforces that this is a key-value pair, adding no new semantic detail beyond what the schema provides. Baseline 3 is appropriate.

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 opens with 'Save data the agent will need to reuse later' and specifies scope across conversation/sessions. It clearly distinguishes from siblings recall (retrieve) and forget (delete) by naming them, making the tool's purpose unambiguous.

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 'Use when you discover something worth carrying forward' with concrete examples, and tells the agent to pair with recall/forget for later retrieval/deletion. It lacks explicit when-not or exclusion cases, but the guidance is clear.

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

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same 5,529 tools, and deep_research overlaps for broad questions. The six prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also have heavily overlapping purposes, making misselection likely.

Naming Consistency4/5

All names use snake_case, which is consistent, and most are verb-first (search, extract, remember, resolve_entity, validate_claim). However, several are noun-phrases (entity_profile, polymarket_arbitrage, recent_alerts, pipeworx_feedback), breaking the verb_noun pattern. The deviations are minor but present.

Tool Count2/5

At 33 tools, the set is well above the 25-tool threshold for 'too many'. The server also spans several unrelated domains—web search, Pipeworx structured data, prediction markets, memory, subscriptions, AI visibility—making the count feel excessive for a coherent purpose.

Completeness4/5

Each sub-domain is well covered: search has search/extract/search_within, prediction markets have research/arbitrage/edges/fill-risk/tracking, subscriptions have subscribe/unsubscribe/list/recent_alerts, and memory has remember/recall/forget. Minor gaps exist (e.g., no way to edit a subscription's parameters, no direct SEC filing content viewer), but agents can work around them via ask_pipeworx.

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