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Glama

Urlscan Io

by io.github.pipeworx-io

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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

A4.7/5.0
Behavior5/5

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

Adds significant value beyond annotations: explains key-value storage, user scoping, and persistence differences between authenticated (persistent) and anonymous (24 hr) sessions. No contradictions with annotations.

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?

Single efficient paragraph with no wasted words. Purpose is front-loaded, and structure flows logically from action to examples to storage details.

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?

Despite no output schema, the description explains storage behavior, scoping, persistence, and references companion tools for full workflow. Sufficient for an agent to understand and use 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 parameters. Description reinforces with usage examples (e.g., 'subject_property') and clarifies value flexibility, adding practical context beyond schema.

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 saves data for reuse across sessions, gives concrete examples of what to store, and explicitly names complementary tools (recall, forget) for differentiation.

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?

Provides specific guidance on when to use (discoveries worth carrying forward) with examples, but lacks explicit when-not-to-use conditions. Mentions pairing with recall and forget for later operations.

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

B3.3/5.0
Disambiguation3/5

Many tools have distinct purposes with thorough descriptions, but there is meaningful overlap among ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research, all of which route questions to data sources. The multiple polymarket tools (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread) also require careful reading to differentiate. The urlscan tools (domain, ip, search, submit, result) are distinct, but the overall set mixes several unrelated domains, increasing misselection risk.

Naming Consistency2/5

Naming is a mix of conventions: short urlscan verbs (domain, ip, search, submit), noun-first names (entity_profile, recent_changes, deep_research), verb_noun names (compare_entities, resolve_entity, generate_llms_txt), and prefixed families (polymarket_*, pipeworx_*). There is no uniform verb_noun pattern or consistent prefix convention across the set. This inconsistency makes it hard to predict what a tool does from its name alone.

Tool Count2/5

With 36 tools, the set is far above the 3-15 range typical for a coherent server, even for a broad data API. The inclusion of meta-tools like discover_tools and suggest_questions suggests the count is so high that agents need help navigating it. The load is compounded by tools spanning urlscan.io, Pipeworx, prediction markets, memory, subscriptions, and feedback, making the server feel like a grab bag rather than a focused service.

Completeness3/5

The urlscan portion is complete for searching, submitting, and retrieving scan results, and the Pipeworx side covers a wide range of data and analysis capabilities. However, the server is named 'Urlscan Io' while most tools are unrelated to urlscan, creating a mismatch between the stated purpose and the actual surface. There are no obvious gaps for the included features, but the lack of a coherent domain makes it hard to assess what 'complete' means for this set.

Resources