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PumpPill — token safety and measured outcomes

X chatter on a contract (members)

x_chatter

MEMBERS: what X has been saying about one contract: posts naming the address in their text, distinct accounts, combined reach, acceleration, and each poster's record on earlier calls. Needs a PumpPill membership (Authorization: Bearer , from pumppill.org/premium) or one x402 payment per call (USDC on Base). Reads X live before answering when the hourly budget allows (the live field says); the stored corpus is always underneath. A SAMPLE of X: zero means PumpPill saw none, and silence on an EVM contract is normal. Each call may spend a live-read budget.

Input Schema

NameRequiredDescriptionDefault
contractNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / contract / default
      Added value: +""
    • removedInput schema / required
      Removed value: -[
      -  "contract"
      -]
  2. Added

TDQS

A3.8/5.0
Behavior5/5

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

Rich disclosure beyond the annotations: it states auth requirements (Bearer claim or one x402 USDC payment on Base), the budget-limited live read with a stored corpus underneath, that each call may spend live-read budget, and crucially that zero means PumpPill saw none and silence on an EVM contract is normal. This is exactly the kind of context an agent needs to interpret results, and it is consistent with readOnlyHint=false and destructiveHint=false.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with 'MEMBERS:' and information-dense, but it is a single cluttered block with heavy parentheticals and semicolons that is harder to parse than it needs to be. Most content earns its place, but the structure is poor.

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 paid, budget-limited live-read tool with no output schema, it covers auth, payment, live-vs-stored semantics, and the meaning of an empty result. It omits what happens when the live budget is exhausted or on error, but is otherwise sufficient for correct invocation and interpretation.

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?

One parameter at 0% schema coverage, so the description must compensate. It clarifies the parameter is one contract address whose text is scanned for the address and that it may be an EVM contract, but gives no format, chain, or casing guidance for the 'contract' field. Partial compensation only.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb+resource: what X has been saying about one contract, and enumerates the metrics returned (posts naming the address, distinct accounts, reach, acceleration, poster record). It is clearly distinguishable from token-snapshot or search siblings in substance, though it never names an alternative explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

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

Usage context is implied ('MEMBERS', social chatter about a contract) and the live-vs-stored reading behavior is described, but there is no explicit when-to-use/when-not or named alternative among the many sibling tools. The agent must infer that this is the social-sentiment lookup.

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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