Machine-readable, append-only signal track record summary (6100+ timestamped signals archived; ~1600 have measured 24h outcomes, the rest still pending or unmeasured — see the actual counts in the response).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that the underlying record is append-only, that many signals are still pending/unmeasured, and that the counts in the description are approximate and should be replaced by actual response values. This goes well beyond a simple 'returns a summary' statement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence with no filler. The core idea 'track record summary' is front-loaded, and the parenthetical adds only essential caveats about scale and measured outcomes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description covers the purpose, the scale, the outcome-status split, and the important caveat to read the actual counts from the response. It could be slightly more explicit about the response structure, but nothing critical appears missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%, so there are no parameter semantics for the description to clarify. The description appropriately focuses on what the returned summary means rather than on inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (signal track record) and the kind of output (a machine-readable summary), including the archive size and outcome breakdown. It is distinguishable from siblings like recent_events or score_mint, though it lacks an explicit action verb and does not directly name an alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used to get track-record statistics, but it gives no explicit guidance about when to use it versus sibling tools. There are no stated conditions, exclusions, or alternative tool names, so the agent must infer suitability from the name and content.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target clearly distinct actions (build, check, score, verify, track), and the detailed descriptions separate chains and purposes. The main ambiguity is between check_robinhood and check_robinhood_honeypot, which both assess Robinhood Chain tokens but via different methods.
Most names follow a verb_noun pattern with a consistent 'check_' family for safety tools. Minor deviations like recent_events, guard_pricing, and track_record break the pattern but are still readable.
Ten tools is well within the ideal range and covers safety checks, signal intelligence, execution, pricing, and verification without feeling padded. Each tool has a distinct role in the guarded-trading workflow.
The surface covers the core non-custodial trading/intelligence loop: assess, score, copy, build, pay, and verify claims. Minor gaps exist around historical signal filtering or arbitrary-wallet deep dives, but they are workarounds rather than dead ends.