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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)

TDQS

A4.7/5.0
Behavior4/5

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

Annotations indicate this is a write operation (readOnlyHint=false), idempotent, and non-destructive. The description adds valuable behavioral context: key-value pairs are scoped by identifier, authenticated users get persistent memory, and anonymous sessions retain data for 24 hours. It does not contradict annotations, though overwrite semantics are not explicitly mentioned.

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 three sentences long, front-loaded with the core purpose, and every sentence provides useful information (when to use, persistence details, sibling pairing). No redundant or filler content.

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 covers all necessary aspects: purpose, usage scenarios, persistence rules, scoping, and relationships with related tools. It is sufficiently complete for an agent to select and invoke it correctly.

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% with descriptions for both key and value, so the baseline is 3. The description adds meaning by framing the parameters as a key-value pair and explaining that the value stores findings, addresses, etc., which enriches the schema's minimal 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?

The description uses a specific verb ('Save') and resource ('data the agent will need to reuse later'), clearly distinguishing the tool from siblings by mentioning it pairs with recall and forget. It also provides concrete examples of what to save, further clarifying purpose.

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?

The description explicitly states when to use the tool ('Use when you discover something worth carrying forward') and names the alternatives recall and forget for retrieval and deletion. It also clarifies scoping and persistence conditions, giving the agent clear decision criteria.

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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Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded share the same routing core, while bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_fill_risk all target prediction-market edge detection. The descriptions are detailed, but an agent must read a lot of nuance to avoid selecting the wrong tool.

Naming Consistency3/5

All names are lowercase snake_case and readable, but the convention is mixed: verb_noun names like encode_html and resolve_entity sit alongside bare verbs like remember and forget, and noun-phrase names like entity_profile, recent_changes, and polymarket_fill_risk. The ask_pipeworx_* and polymarket_* families are internally consistent, but there is no single predictable pattern across the whole set.

Tool Count2/5

At 33 tools this exceeds the 25+ threshold for a heavy set. The bloat is especially noticeable because the server is named Htmlentities yet only encode_html and decode_html relate to that purpose; the rest are unrelated Pipeworx research, prediction-market, memory, and subscription utilities.

Completeness4/5

The Pipeworx surface is broadly complete: ask/grounded/deep_research/discover/suggest cover data access, entity_profile/compare_entities/recent_changes/validate_claim cover entity workflows, and subscriptions and memory have create/list/delete lifecycles. Minor gaps such as no update operation for subscriptions or memories are workable, and encode/decode fully covers the literal Htmlentities purpose.