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Space Feeds

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

A4.1/5.0
Behavior3/5

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

The description adds useful behavioral context beyond annotations: OAuth requirement, sms verification and 10/day cap, and type-specific parameter semantics. However, it omits the webhook delivery channel (though the schema covers it) and does not clarify idempotent semantics despite the idempotentHint=true annotation. There is no direct contradiction with any annotation.

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 four sentences and densely packs valuable information without fluff. It front-loads the primary purpose and then efficiently covers types and delivery channels. Though slightly long, every clause earns its place.

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 tool with nested parameters, no output schema, and mixed delivery options, the description covers the essential context: types, parameters, authentication, feed, and email/sms. Minor gaps (webhook details, idempotency) are partially filled by the schema annotations, leaving the tool adequately self-contained.

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%, so the baseline is 3. The description adds concrete examples for each type (e.g., items:["5.02"] = officer change, topic:"fed", series_id:"UNRATE") and clarifies delivery constraints (verified phone, cap), enriching the parameter meaning beyond raw schema definitions.

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 opens with a specific verb and resource: 'Create a proactive monitoring subscription to a live-data event stream.' It clearly distinguishes this from sibling tools like list_subscriptions and unsubscribe, and enumerates supported subscription types, making the purpose unmistakable.

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 clear context for when to use the tool (to establish proactive monitoring), prerequisites (requires Pipeworx OAuth account, anonymous/BYO cannot persist), and delivery options. It does not explicitly name alternatives like 'use recent_alerts for one-time pulls,' but the distinction is implied through the 'pull via recent_alerts' phrasing.

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

A3.6/5.0
Disambiguation2/5

Several tools occupy nearly the same niche: ask_pipeworx_beta is explicitly an identical duplicate of ask_pipeworx when no experiment is active, and ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all overlap as question-answering entry points. Other families like entity_profile vs compare_entities vs recent_changes and ai_visibility_check vs scan_competitor_ai_presence also blur together despite long disambiguating descriptions.

Naming Consistency4/5

All tool names use a clean, readable snake_case style, and there are strong prefix families like ask_pipeworx, polymarket_, list_, and scan_. However, the set is not uniformly verb_noun: entity_profile, deep_research, recent_alerts, pipeworx_trending, and several others are noun phrases rather than actions, so the pattern is mostly consistent but not strict.

Tool Count2/5

34 tools is well beyond the 25+ threshold where a server starts feeling bloated, and the server name 'Space Feeds' suggests a narrow niche while most of the surface is a general data research, prediction-market, memory, and subscription platform. Each tool may be useful, but as a set the scope is sprawling rather than focused.

Completeness4/5

The major workflows have good lifecycle coverage: data lookup and grounded verification, entity resolution and profiling, prediction-market analysis, memory (remember/recall/forget), subscriptions (subscribe/list/unsubscribe/recent_alerts), and feed reading (list/read/fetch) are all represented. Minor gaps include a direct pipeworx:// citation reader and feed curation or management operations, but agents can work around those.

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