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Remember

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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

A4.2/5.0
Behavior4/5

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

The description adds behavioral context beyond the annotations: persistence differences for authenticated vs. anonymous sessions, scoping by identifier, and the key-value storage model. While annotations already indicate idempotent and non-destructive behavior, the description clarifies the memory retention policy.

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 about 80 words and well-structured. It front-loads the main purpose, then gives usage context, storage details, and a pointer to related tools. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple write tool, the description is complete: it covers what to store, when to use it, how data is persisted, and how it relates to recall and forget. The lack of an output schema is acceptable since this is a write operation, and the schema already documents both required parameters.

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 coverage is 100%, so the baseline is 3. The description reinforces the key-value model and notes that 'value' can be any text, but it does not add substantial meaning beyond the schema's descriptions of 'key' and 'value' parameters.

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's function with a specific verb and resource: 'Save data the agent will need to reuse later' as a key-value pair. It distinguishes itself from siblings by explicitly mentioning paired tools (recall, forget) and giving concrete examples of what to store.

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?

The description provides explicit 'when to use' guidance: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also names complementary tools (recall, forget) for retrieval and deletion, though it does not explicitly state when not to use the tool.

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

B3.2/5.0
Disambiguation2/5

Several tight clusters of overlapping tools: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all answer factual questions (beta is currently identical to stable per its own description), six Polymarket tools all surface betting/edge opportunities, and ai_visibility_check vs scan_competitor_ai_presence duplicate functionality. Despite long descriptions, an agent could easily pick the wrong tool.

Naming Consistency2/5

Naming mixes bare single-word nouns (address, block, node, stats, transaction), bare verbs (remember, recall, forget, subscribe), verb_noun compounds (generate_llms_txt, scan_dependency, compare_entities), and prefixed families (polymarket_*, pipeworx_*, ask_pipeworx*). Some clusters are internally consistent, but the blockchain endpoints break the verb convention entirely and there is no uniform pattern across the set.

Tool Count2/5

36 tools spanning at least six unrelated domains — blockchain explorer, structured-data research, prediction markets, AI visibility, memory, and subscriptions — is too heavy for a coherent server. The count exceeds the 25+ threshold and reflects scope creep rather than a focused purpose.

Completeness3/5

The Pipeworx research surface is near-complete (discover/resolve/ask/ground/verify/search-within plus entity/profile/compare), prediction markets are exhaustively covered, and memory/subscriptions have full lifecycles. But the server's namesake domain — Blockchair blockchain data — is thin at just five basic queries with no fee estimation, mempool, or deeper chain analytics, leaving notable gaps in the surface implied by the server name.