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Gov Bids

by io.github.pipeworx-io

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Idempotent

Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = officer change), "polymarket_edge" (Polymarket↔Kalshi cross-venue mispricings — params:{topic:"fed"}), "fred_series" (new FRED observations — params:{series_id:"UNRATE"}). Delivery channels: feed (always on — pull via recent_alerts or GET registry.pipeworx.io/alerts.json), and optionally email (set delivery:{email:"you@x.com"}) or sms (delivery:{sms:"+15551234567"} — phone must be verified at /account first; 10/day cap).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesSubscription type.
paramsYesType-specific filter. sec_8k: {ticker:"AAPL", items?:["5.02","1.01"]}. polymarket_edge: {topic:"fed", min_spread_bps?:500}. fred_series: {series_id:"UNRATE"}. patent_grant: {applicant:"Apple Inc."}. clinical_trial: {sponsor?:"Pfizer", condition?:"lung cancer", phase?:"PHASE3"} (sponsor or condition required).
deliveryNoOptional delivery channels in addition to the always-on persistent feed. {email:"you@x.com"} sends a templated alert per fired event. {sms:"+15551234567"} sends an SMS per event — must match the verified phone on the caller's account (verify at https://pipeworx.io/account first; 10/day cap). {webhook:"https://..."} POSTs each event JSON to your endpoint, HMAC-signed — the response includes delivery.webhook_secret (whsec_…) ONCE; verify X-Pipeworx-Signature = sha256 HMAC of "<X-Pipeworx-Timestamp>.<raw body>". Auto-disabled after 10 consecutive failing runs.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.8/5.0
Behavior1/5

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

The description claims each call creates a new subscription ('Returns the new subscription id'), implying side effects. However, annotations mark idempotentHint=true, which means the tool should be safe to retry without side effects. This contradiction indicates a serious inconsistency. The description does disclose delivery limitations and verification requirements, but the contradiction overrides any positive aspects.

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

Conciseness4/5

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

The description is front-loaded with the main purpose. It is somewhat lengthy but every sentence adds necessary detail. No filler or tautology. The structure is logical: purpose, return value, requirements, types, delivery. Could be slightly condensed but remains efficient.

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?

The description covers all essential aspects: creation action, return value, authentication prerequisites, supported subscription types with examples, delivery channels with constraints, and failure behavior (webhook auto-disable). There is no output schema, but the description adequately explains the return value. The tool is fully described for correct invocation.

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 description coverage is 100%, so the schema already documents each parameter. However, the description adds significant value by providing usage examples for each type (e.g., sec_8k with items), explaining delivery channel constraints (SMS cap, webhook signing secret), and clarifying prerequisites. This goes well beyond the schema's basic 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 the verb 'Create' and resource 'proactive monitoring subscription to a live-data event stream'. It clearly distinguishes from sibling tools like list_subscriptions and unsubscribe by stating it creates a new subscription. Examples of supported types and delivery channels further clarify the purpose.

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 when to use (proactive monitoring) and prerequisites (Pipeworx OAuth account). It lists supported types with contextual examples. However, it does not explicitly guide when not to use or compare to siblings like recent_alerts, though the context is clear enough for most use cases.

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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the multiple Pipeworx query variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and multiple Polymarket tools could cause some confusion. However, descriptions are detailed enough to differentiate them.

Naming Consistency3/5

Naming patterns are mixed: some tools use verb_noun (list_subscriptions, open_bids_search), others use noun_phrase (entity_profile, bet_research), and cases are inconsistent (snake_case vs underscores). While not chaotic, the lack of a strong consistent pattern reduces coherence.

Tool Count2/5

33 tools is high, and the server's name 'Gov Bids' suggests a focused scope, but most tools are unrelated (AI visibility, npm packages, general data queries). The tool count feels excessive for a focused server, and the scope is too broad.

Completeness4/5

For a general-purpose data server, the tool set is quite comprehensive across multiple domains (SEC, FDA, economics, prediction markets, etc.). Minor gaps exist (e.g., government contracts beyond bids), but overall coverage is strong.

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