Skip to main content
Glama

MetricDuck — Financial Analysis

by com.metricduck
OverviewDiscussionsAdmin

Screen Companies by Metrics

screen_companies
Read-only

Screen 5,500+ US companies by financial metrics. Find stocks matching quantitative criteria.

Metric IDs, period types (incl. growth: period_type="ttm.yoy") and sector codes are listed on the filters / sectors parameters. LATEST-snapshot values; full history via get_metric_history.

Tag filtering (required_tags / excluded_tags) selects by business-model classification; unclassified companies are excluded from tag-filtered results.

Use Cases:

  • "High ROIC tech stocks" -> filters=[{metric_id:"roic", operator:"gt", value:0.15}], sectors=["TECH"]

  • "Undervalued profitable industrials" -> filters=[{metric_id:"pe_ratio", operator:"lt", value:15}, {metric_id:"pe_ratio", operator:"gt", value:0}], sectors=["IND"]

  • "Revenue growing >10% YoY" -> filters=[{metric_id:"revenues", operator:"gt", value:0.10, period_type:"ttm.yoy"}]

  • "AI infrastructure companies not exposed to China supply chain" -> required_tags=["ai_ml_infrastructure"], excluded_tags=["china_supply_chain_heavy"]

  • "Profitable subscription businesses" -> filters=[{metric_id:"net_margin", operator:"gt", value:0.10}], required_tags=["subscription_recurring"]

To screen by filing SIGNALS (tone, covenant risk, material weakness, etc.), use screen_filing_signals — a universe-correct cross-company signal screen — then intersect with a metric screen here.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 20, max 50). Responses cap at ~20K chars: a lower limit or stricter filters keep them whole.
filtersYesMetric filters
sectorsNoSector codes: TECH, FIN, HEALTH, CONS_STAPLES, CONS_DISC, IND, ENERGY, UTIL, RE, MAT, COMM.
sort_byNoMetric ID to sort by (default: 'market_cap')
excluded_tagsNoClassification tags results must NOT have (e.g., ['china_supply_chain_heavy', 'regulated_industry']).
required_tagsNoClassification tags all results must have (e.g., ['ai_ml_infrastructure', 'subscription_recurring']). Tags: cloud_infrastructure, saas_enterprise, saas_smb, marketplace_platform, semiconductor_design, semiconductor_foundry, financial_services_traditional, insurance_carrier, investment_management, pharmaceutical_discovery, medical_devices, retail_physical, ecommerce_direct, media_streaming, subscription_recurring, usage_based, hardware_sale, transaction_fee, advertising_based, government_contract, services_project, ai_ml_core_product, ai_ml_infrastructure, semiconductor_advanced_node, data_center_hyperscale, cybersecurity, autonomous_vehicles, electric_vehicle, renewable_energy, biotech_genomics, mrna_platform, robotics_automation, enterprise_b2b_large, smb_focused, consumer_direct, developer_platform, china_revenue_heavy, china_supply_chain_heavy, us_domestic_only, global_diversified, regulated_industry, export_controlled, dual_use_technology, foreign_private_issuer, holding_company_structure.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, a low bar. The description adds non-obvious behavior beyond them: results are LATEST-snapshot only, an unsupported metric+period combination returns a 422 naming the supported set, tag-filtered results silently exclude unclassified companies, and responses cap near 20K chars, forcing lower limits or stricter filters.

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?

Front-loads purpose, then scoping caveats, then use cases; no sentence is filler and every example teaches a distinct filter pattern (valuation, growth, tag intersection). It runs long, but the length is driven by the genuinely large parameter surface rather than padding.

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 6-parameter screening tool with no output schema, the description covers invocation, valid value sources, error behavior, and tool routing well. The one gap is that it never says what a result row contains (which company fields come back), though the sibling set and schema largely imply it.

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 a 3 would be the floor, but the description exceeds it: it explains where metric IDs, period types (including the growth form period_type="ttm.yoy") and sector codes live, and the examples demonstrate the nested filter array shape, operator usage, and required_tags/excluded_tags pairing that the schema only gestures at.

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?

States a specific verb and resource with scope ('Screen 5,500+ US companies by financial metrics') and immediately distinguishes itself from the sibling signal-screening tool by naming screen_filing_signals and its domain. An agent can pick this over search_companies or get_financials without opening any schema.

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

Usage Guidelines5/5

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

Gives explicit when-to-use via five worked use cases and an explicit when-to-use-the-other-tool rule ('To screen by filing SIGNALS ... use screen_filing_signals ... then intersect with a metric screen here'). It also points to get_metric_history for the full-history case, which is a real alternative an agent would otherwise guess at.

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

All MetricDuck — Financial Analysis toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources