Skip to main content
Glama

Opencellid

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds self-aggregating signal, no PII, caching details. No contradictions.

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?

Concise, well-structured: purpose first, then uses, then technical details. Every sentence adds value.

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?

No output schema but describes return values (top tools, packs, volume). Includes caching and privacy details. Complete for its purpose.

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 covers window param with description. Description adds semantic context: shorter windows for hot, longer for steady-state. Adds value 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?

Clearly states it returns top tools, packs, call volume over a window. Differentiates from siblings by focusing on aggregated usage trends.

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?

Explicitly lists three use cases: discovering hot data sources, confirming canonical tool, aligning use case. Lacks explicit when-not or alternatives, but context is helpful.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Opencellid toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation3/5

While many tools have distinct purposes, there is significant overlap among the Pipeworx query tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and suggest_questions all route questions to the same underlying sources. Also, several Polymarket analysis tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) have overlapping purposes of finding and analyzing betting edges. This overlap could cause misselection, though the detailed descriptions provide some disambiguation.

Naming Consistency3/5

The naming is mostly readable but mixes conventions: pipeworx tools use snake_case (ask_pipeworx, pipeworx_feedback), while the prediction market tools use camelCase (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage). Verbs vary widely: ask, get, scan, generate, recall, remember, resolve, search, subscribe, unsubscribe, validate. There's a mix of domain-specific prefixes and generic actions without a clear uniform pattern.

Tool Count2/5

33 tools is a very large surface for an MCP server, even one covering a broad domain. While the server claims to cover many data sources (financial, pharma, economics, prediction markets, cell towers, etc.), the high count risks cognitive overload and makes it hard for an agent to discover the right tool. Many tools appear to be meta-tools (suggest_questions, discover_tools, remember/recall/forget) that add to the count without adding core domain operations.

Completeness4/5

The server covers a wide range of functionality: data lookup, entity resolution, research, prediction market analysis, memory management, subscriptions, and even niche features like AI visibility checking and llms.txt generation. Given the breadth, most use cases have coverage. However, the completeness is uneven—the OpenCellID tools (cells_in_area, get_cell) are very minimal with just two basic lookups and no batch/history features, while the Pipeworx side has extensive coverage. Some operations like updating or deleting entities are absent, but that may not be expected for a read-heavy data server.

Resources