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Glama

imgflip

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. Added

TDQS

A4.5/5.0
Behavior4/5

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

Adds valuable context beyond annotations: persistence duration (24 hours for anonymous, persistent for authenticated users) and scoping by identifier. The description does not contradict annotations (readOnlyHint=false, idempotentHint=true), though it could mention overwrite behavior or size limits. Still, it discloses key behavioral traits not present in structured fields.

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?

Five concise sentences that front-load the primary purpose, then explain when to use, storage mechanics, retention behavior, and related tools. No filler or redundant information; every sentence serves a distinct function.

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 two-parameter tool with no output schema, the description is fully complete. It covers purpose, usage triggers, storage scoping, retention periods, and links to sibling operations. There are no significant informational gaps for an agent to invoke this tool effectively.

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?

Schema already provides 100% coverage with clear descriptions and examples for both key and value. The description's examples ('a resolved ticker', 'target address') reinforce usage but do not add new parameter-level semantics beyond what the schema offers. Baseline 3 is appropriate given high schema coverage.

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 a specific action ('Save data the agent will need to reuse later') with a concrete resource (key-value store scoped by identifier). It distinguishes itself from sibling tools by explicitly mentioning recall and forget for retrieval and deletion, making the 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 Guidelines5/5

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

Provides explicit 'when to use' guidance with concrete examples (resolved ticker, target address, user preference, research subject) and states the benefit ('so you don't have to look it up again'). It also names complementary tools (recall, forget) for related operations, offering clear situational context.

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

A3.5/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route to the same 5,743-tool catalog, and the six Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) all target prediction-market analysis. Long descriptions clarify some boundaries, but an agent could easily misroute a query to the wrong variant.

Naming Consistency3/5

Names are mostly snake_case and readable, with consistent families like ask_pipeworx_* and polymarket_*, but conventions vary widely: verb-first (validate_claim, compare_entities, generate_llms_txt), noun-first (entity_profile, bet_research, recent_changes), and standalone verbs (remember, recall, forget). The pipeworx_ prefix is applied inconsistently, and get_memes sits apart from the data-tool naming style.

Tool Count2/5

32 tools exceeds the comfortable range and the count is inflated by near-duplicate variants (three ask_pipeworx versions, six Polymarket tools). The scope mismatch compounds the issue: the server is named imgflip but nearly all tools belong to Pipeworx data/prediction-market functionality, so the set feels bloated rather than deliberately scoped.

Completeness1/5

As an imgflip/meme server the surface is severely incomplete: get_memes explicitly refers to caption_image for creating memes, but that tool is absent, creating a dead end. The remaining tools cover a broad but unrelated Pipeworx data domain, so no coherent domain gets full lifecycle coverage and the tool set fails its apparent core purpose.

Resources