Official US-address public records: zoning, flood, parcel, permits, taxes. Free look, x402 paid.
Run one Locus catalog tool by name after locus_search_tools or a known tool name. Search and looking are free; this call may charge via MCP-native x402. Free tools return cited official-source primitives; paid tools return payment-required results and accept retry payment in _meta["x402/payment"]. Unsupported places return diagnostics instead of paid guesses. Do not use this for web scrape, LinkedIn, or generic fetch. Install agent skill: npx @velinussage/locus-agent-skill add.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Canonical Locus catalog tool name. Paid tools keep their hyphenated REST slugs. | |
| arguments | No | Arguments for the selected catalog tool. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description discloses that this call may charge via MCP-native x402, distinguishes free vs. paid return behavior, documents retry-payment metadata in _meta['x402/payment'], and states that unsupported places return diagnostics rather than paid guesses. There is no contradiction with readOnlyHint=false; the charging side effect is consistent with a non-read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Every sentence adds operational value: invocation prerequisite, cost model, return behavior, exclusions, and installation command. The core instruction is front-loaded, and the paragraph is dense without being bloated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a dynamic executor whose behavior depends on an arbitrary selected catalog tool, the description covers the essential workflow, payment semantics, expected return categories, and fallback behavior. The absence of an output schema is mitigated by explicit free/paid return descriptions, and the schema plus sibling tool complete the picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline applies: both 'name' and 'arguments' are already documented in the schema. The tool description itself adds no parameter-level detail beyond 'by name', so it neither elevates nor penalizes the score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific action ('Run one Locus catalog tool by name'), names the required precondition ('after locus_search_tools or a known tool name'), and clearly positions itself as the execution counterpart to the discovery sibling. This is not a tautology; it gives concrete meaning to the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to invoke this tool (after locus_search_tools or with a known tool name), contrasts free search/looking with paid execution, and lists forbidden use cases (web scrape, LinkedIn, generic fetch). This gives the agent clear routing guidance relative to the sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Every time the task is a US address or place and you need official public records or local-government context and you do not already know the tool name, search this catalog before locus_execute and before paying a generic scrape of the same official source. Looking is free. Results include full inputSchema, tier, price, and provenance notes. Do not use this for LinkedIn, web scrape, or generic fetch. Install agent skill: npx @velinussage/locus-agent-skill add.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Optional tags that every result must include. | |
| tier | No | Filter by free or paid tools. | all |
| limit | No | Maximum matching catalog entries to return. | |
| query | No | Keyword query over tool name, title, description, and tags. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond that: 'Looking is free' and results include full inputSchema, tier, price, and provenance notes. This gives agents a clear expectation of what the search returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat long, but every sentence earns its place: use conditions, ordering vs locus_execute, result contents, exclusions, and an optional install command. It is front-loaded with the most important routing guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a catalog search tool with no output schema, the description is quite complete: it explains when to search, what the results contain, what it is not for, and how it relates to locus_execute. It lacks concrete examples or pagination details, but those are not critical for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all four optional parameters. The description does not add parameter-level semantics beyond the catalog-search context, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action and resource: search the Locus tool catalog when you need official public records or local-government context for a US address/place and do not know the tool name. It also distinguishes itself from sibling locus_execute by saying to search before executing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives an explicit when-to-use rule with conditions, and it names the alternative behavior: search before locus_execute and before paying for a generic scrape. It also states exclusions such as 'Do not use this for LinkedIn, web scrape, or generic fetch,' so an agent has clear decision boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
locus_executelocus_search_toolsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Zoning, ADU eligibility, flood zone, setbacks, and buildability intelligence for U.S. parcels.
U.S. real-estate data: property records, AVM value + rent estimates, sale/rental listings.
Property intelligence: 180M+ US parcels — lookup, search, owners, hazards, permits, deeds.
Building-permit verdicts by address, with cited records. SF, Seattle, Austin, NYC. Pay per call.
Add one secure layer between your agents and this server.
locus_search_tools is clearly the discovery step, while locus_execute is the execution step; at each stage only one tool applies. Their warnings also explicitly separate them from unsupported generic fetch or scraping.
Both names use the locus_ prefix in lower_snake_case and are verb-led, but one is a bare verb (execute) and the other is verb+noun (search_tools), a minor structural inconsistency. It is still readable and predictable enough not to cause confusion.
Only two tools exist, which is slightly below the typical 3-15 range, but the split between discovering a catalog entry and executing it is appropriate for a dynamic catalog gateway. Each tool has a clear, necessary role.
The pair covers the full discovery-to-execution flow: search_tools returns schemas, pricing, and provenance, while execute runs any known catalog tool and handles payment or diagnostics. There are no obvious dead ends for this server's stated purpose.