2,029 DISA STIG rules with official check/fix text, CCI mappings, and .ckl checklist export.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.3/5 across 4 of 4 tools scored.
Each tool targets a distinct operation: listing benchmarks, searching rules, retrieving rule detail, and exporting a checklist. There is no overlap or ambiguity between them, even for an agent unfamiliar with the domain.
All tool names follow the verb_noun pattern in snake_case: list_stig_benchmarks, search_stig, get_stig_rule, and export_stig_checklist. The naming is uniform and predictable.
With four tools, the server is tightly scoped to the STIG workflow: discover benchmarks, search rules, inspect details, and generate the .ckl artifact. This is an appropriate size for a focused reference/export tool.
The tool set covers the full read-and-export lifecycle for STIG benchmarks: listing available benchmarks, searching and retrieving rule details, and producing the standard checklist output. There are no obvious gaps or dead ends for the stated purpose.
export_stig_checklistExport a DISA .ckl checklistAGenerate a DISA STIG Viewer checklist (.ckl XML) for a benchmark, optionally filtered by severity and pre-populated with findings. This is the artifact an assessment actually hands over - STIG Viewer opens it, eMASS ingests it, and a POA&M is written from it. Rules you do not supply a status for come out as Not_Reviewed. Call this when the user wants a checklist, a scan result recorded, or evidence to submit, rather than just to read a rule.
| Name | Required | Description | Default |
|---|---|---|---|
| host_ip | No | ||
| product | Yes | Which benchmark to build the checklist from | |
| findings | No | Per-rule results. Anything omitted stays Not_Reviewed - an unreviewed rule must never be reported as passing. | |
| severity | No | Limit to one severity, e.g. high for a CAT I-only checklist | |
| host_fqdn | No | ||
| host_name | No | Asset hostname recorded in the checklist |
| Name | Required | Description |
|---|---|---|
| ckl | Yes | The .ckl XML. Write it to filename rather than pasting it into a reply. |
| note | Yes | |
| filename | Yes | |
| benchmark | Yes | |
| rule_count | Yes | |
| status_counts | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint=true, destructiveHint=false), the description discloses a key behavioral trait: rules without a supplied status default to Not_Reviewed, preventing silent false positives. It also clarifies the checklist is the actual assessment artifact ingested by downstream systems, which adds context annotations do not provide.
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?
Four tight sentences front-load the action and rationale. Each sentence earns its place: generation, downstream significance, default status behavior, and usage trigger. No filler or repetition of structured fields.
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?
The description covers purpose, output artifact context, default handling, optional filters, and when to invoke the tool. With an output schema present, return-value details are unnecessary, and the remaining parameter meanings are either clear from names or schema descriptions.
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 coverage is 67%, so the description partially carries parameter meaning. It refers to severity filtering and pre-population via findings, but these largely mirror the schema's existing parameter descriptions. It adds no new clarity for host_ip or host_fqdn, which lack schema descriptions, leaving a modest gap.
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 opens with a specific verb-resource pair: 'Generate a DISA STIG Viewer checklist (.ckl XML) for a benchmark.' It also distinguishes the tool from read-only rule lookups by positioning it as the artifact handed over to STIG Viewer/eMASS, clearly separating it from sibling tools like get_stig_rule.
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?
The description explicitly states when to call it: 'when the user wants a checklist, a scan result recorded, or evidence to submit.' It also gives a when-not signal ('rather than just to read a rule'), though it does not name the exact sibling tool to use instead; naming get_stig_rule would make it fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_stig_ruleGet one STIG rule in fullAGet the complete detail for one DISA STIG rule by its SV id (from search_stig): the requirement discussion, the exact check procedure an assessor runs, the fix text, severity, NIST control mapping, and whether it is SCAP-automatable. Call this when the user needs to implement or verify a specific rule.
| Name | Required | Description | Default |
|---|---|---|---|
| sv_id | Yes | Rule id, e.g. "SV-257777r991589_rule" (fragments matched if unique) |
| Name | Required | Description |
|---|---|---|
| sv_id | Yes | |
| title | Yes | |
| nist_id | Yes | CCI identifier, e.g. CCI-000366. |
| product | Yes | |
| category | Yes | |
| fix_text | Yes | |
| severity | Yes | |
| benchmark | Yes | |
| check_text | Yes | DISA's own check procedure - quote it rather than paraphrasing. |
| description | Yes | |
| automation_level | Yes | |
| automation_source | Yes | |
| severity_category | Yes | CAT I / II / III, the vocabulary assessors use. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, establishing safe read behavior. The description adds value by detailing exactly what content the user can expect (discussion, check, fix, severity, NIST mapping, SCAP-automatable), which sets proper expectations. No contradiction.
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 two sentences: the first states the action and output, the second gives the usage trigger. No wasted words.
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 one-parameter tool with an output schema, the description is complete. It provides purpose, usage context, and input-source guidance. No gaps remain.
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?
The schema covers the single parameter fully, and the description adds an important contextual clue that the sv_id originates from search_stig. This reinforces but slightly extends the schema, helping the agent know where to obtain the value.
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 uses the specific verb 'Get' with a clear resource 'one DISA STIG rule' and the method 'by its SV id'. It explicitly enumerates the detail fields returned, distinguishing it from sibling tool search_stig which presumably returns a list or summary.
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?
The description includes the explicit trigger 'Call this when the user needs to implement or verify a specific rule' and mentions the source of the id '(from search_stig)'. It doesn't state explicit when-not scenarios or name alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_stig_benchmarksList covered STIG benchmarksAList the DISA STIG benchmarks this server covers, with versions, rule counts, and severity breakdowns. Call this first when unsure whether a platform is covered.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description aligns by saying 'list'. It adds value by specifying the return contents (versions, rule counts, severity breakdowns), which goes beyond the annotations. No contradiction.
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 a single sentence that conveys purpose, output content, and usage guidance without wasted words. It's front-loaded with the main action.
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 simple list tool with no parameters and no output schema, the description provides sufficient context: what it lists, what each entry includes, and when to call it. Could be slightly more detailed on return structure but is adequate.
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?
The input schema has zero parameters, so schema coverage is 100%. With no parameters to explain, the description doesn't need to provide parameter semantics; baseline for 0 params is 4.
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 uses a specific verb 'List' and identifies the resource 'DISA STIG benchmarks' with scope 'this server covers', and adds detail about versions, rule counts, and severity breakdowns. This clearly differentiates it from sibling tools like get_stig_rule or search_stig.
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 instructs to 'Call this first when unsure whether a platform is covered', giving a clear when-to-use scenario. It doesn't mention when not to use or alternatives, so not a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_stigSearch DISA STIG rulesASearch DISA STIG hardening rules across RHEL 8, RHEL 9, Windows 11, Windows Server 2022, and Cisco IOS Router NDM benchmarks - by keyword (matched against rule IDs and titles first, then descriptions), filterable by product, severity (high/medium/low, mapping to CAT I/II/III), category, and automation level. Call this when the user asks how to harden one of these platforms, what a STIG requires, or which rules cover a topic like SSH, passwords, or auditing. Returns summaries; use get_stig_rule for full check and fix text.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results per page (default 20) | |
| query | Yes | Keyword or rule id fragment, e.g. "ssh banner" or "SV-257777" | |
| format | No | "summary" (default) returns each rule inline as JSON. "links" returns MCP resource links, which hosts can render as pickable items the user opens on demand. Not smaller - the title and severity you need in order to choose are the bulk of either shape - so choose on how the client presents results, not to save tokens. | |
| offset | No | Skip this many matches. Pass the next_offset from a previous response to reach results beyond the first page. | |
| product | No | Limit to one benchmark | |
| severity | No | high=CAT I, medium=CAT II, low=CAT III | |
| automation | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/destructive safety, so the description need not restate those. It does add useful search behavior (match order, summary-only returns), but it also claims the tool is 'filterable by ... category' even though no category parameter exists in the schema, which undermines transparency.
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 compact and front-loaded, with the main action in the first sentence. It packs platform list, filters, use cases, and an alternative into two sentences without obvious filler, though the platform enumeration makes it a bit dense.
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 7-parameter search tool with no output schema, it covers use cases and the summary-vs-full distinction well. However, it does not describe what fields a 'summary' contains, and the category mention adds confusion rather than completeness.
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 coverage is high (86%), so baseline is 3, but the description adds little beyond the schema's existing parameter descriptions and introduces a misleading 'category' filter that is not a supported parameter. The only added nuance is keyword matching order, which is minor.
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 opens with a specific verb and resource: 'Search DISA STIG hardening rules across [five named benchmarks]', clearly stating the tool's scope. It also distinguishes itself from the get_stig_rule sibling by noting it 'Returns summaries; use get_stig_rule for full check and fix text.'
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 explicit when-to-use guidance: 'Call this when the user asks how to harden one of these platforms, what a STIG requires, or which rules cover a topic...'. It also names the alternative get_stig_rule for full details, satisfying the when/when-not/alternatives test.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming 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 or an account that owns the GitHub organization, then choose Claim with GitHub.
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
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.
No comments yet. Be the first to start the discussion!