Which integrations are available for this server?

Enables AI agents built with LangChain to search and analyze government tenders. Allows LangGraph-based agents to use the tender tools for government opportunity intelligence. Integrates with OpenAI Agents SDK to search and score government contract opportunities.

How do I use Tender MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Tender MCP Find active cybersecurity tenders in the UK from the last 14 days." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Tender MCP

by OjasKord

Overview Schema Source Related Servers Score Discussions Admin

JavaScript

Remote

Tender MCP — Government Opportunity Intelligence for AI Agents

ToolRank

Find, score, and monitor government contract opportunities across UK, EU, and US. AI-powered relevance scoring so your agent surfaces the right opportunities — not just keyword matches.

Free tier: 10 searches/month. No API key required. Just connect and go.

Quick Start

{
  "tender": {
    "url": "https://tender-mcp-production.up.railway.app"
  }
}

Or via Smithery:

npx -y @smithery/cli@latest mcp add OjasKord/tender-mcp

Related MCP server: Government Contracts MCP

Harness Integration

Claude Code / Claude Desktop (.mcp.json)

{
  "mcpServers": {
    "tender": {
      "type": "http",
      "url": "https://tender-mcp-production.up.railway.app"
    }
  }
}

LangChain (Python)

from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
    "tender": {
        "url": "https://tender-mcp-production.up.railway.app",
        "transport": "http"
    }
})
tools = await client.get_tools()

OpenAI Agents SDK (Python)

from agents import Agent, HostedMCPTool
agent = Agent(
    name="Assistant",
    tools=[HostedMCPTool(tool_config={
        "type": "mcp",
        "server_label": "tender",
        "server_url": "https://tender-mcp-production.up.railway.app",
        "require_approval": "never"
    })]
)

LangGraph

Same as LangChain above — langchain-mcp-adapters works with LangGraph natively.

Why Use This

Any business that sells to government needs to monitor tender opportunities. But searching three separate government portals daily, reading hundreds of notices, and manually judging relevance takes hours. Tender MCP does it in seconds — search UK, EU, and US simultaneously, then let AI score which opportunities actually match your capabilities.

Tools

`search_tenders`

Search active government tenders across UK Contracts Finder, EU TED, and US SAM.gov simultaneously. Returns normalised results with source_url and checked_at.

{ "keyword": "cybersecurity", "sources": ["uk", "eu"], "days_old": 14 }

`get_tender_detail`

Full details for a specific tender — complete description, all deadlines, contact details, award criteria.

{ "tender_id": "ocds-h6vhtk-...", "source": "uk" }

`score_tender_fit` (AI-powered — NOT a keyword match)

AI scores each tender 0-100 against a company capability profile. Returns BID/INVESTIGATE/SKIP recommendation with specific reasons. Saves hours of manual review.

{
  "tenders": [...],
  "company_profile": "We are a 50-person UK cybersecurity firm specialising in penetration testing and SOC services for public sector clients. We hold SC clearance.",
  "min_score": 60
}

`get_daily_digest` (Paid only)

All new tenders published in the last 24 hours matching your keywords. Run daily as a morning briefing.

{ "keywords": ["cybersecurity", "managed SOC", "penetration testing"] }

`get_award_history` (Paid only)

Past award winners for a keyword. Use for competitive intelligence before bidding.

{ "keyword": "cybersecurity", "sources": ["uk"] }

Recommended Workflow

Find and score opportunities (3 calls):

search_tenders — find active tenders matching your sector
score_tender_fit — AI ranks by relevance, filters noise
get_tender_detail — full details on top matches

Daily monitoring (1 call):

get_daily_digest — new tenders every morning before competitors see them

Data Sources

Source	Coverage	Update Frequency
UK Contracts Finder (contractsfinder.service.gov.uk)	All UK public sector contracts	Real-time
EU TED (ted.europa.eu)	All EU member state procurement	Real-time
US SAM.gov (sam.gov)	All US federal opportunities	Daily

Every response includes source_url and checked_at.

Pricing

Plan	Searches	Price
Free	10/month	No API key required
Starter	500-call bundle	$8
Pro	2,000-call bundle	$28

Upgrade at kordagencies.com

Reliability

Uptime monitored every 5 minutes
Version history in CHANGELOG.md
Health endpoint: GET /health
Note: Government portal APIs experience occasional downtime — errors include explanation and retry guidance

Legal

Tender data sourced directly from official government portals. We do not log or store your query content. Always verify tender deadlines and details directly with the contracting authority before submitting a bid — deadlines change. Results are for informational purposes only. Maximum liability limited to 3 months subscription fees. Full terms: kordagencies.com/terms.html

Connect

Website: kordagencies.com
Contact: ojas@kordagencies.com

Available Tools

2 tools

get_tender_intelligenceAInspect

Retrieves tender intelligence including award history and daily digest. Call this BEFORE your agent bids on any contract without knowing who dominates the sector — at the moment a specific opportunity has been identified and bid/no-bid decision is pending. Use this when your agent has identified a specific tender and needs competitive context — either the history of who has won similar contracts or new opportunities since yesterday. AWARD_HISTORY: past contract winners for a keyword. DAILY_DIGEST: all new tenders last 24h for monitored keywords. Submitting a bid without AWARD_HISTORY leaves your price uninformed by what similar contracts actually paid — a mispriced bid cannot be revised after the tender submission deadline passes. Do not bid without running AWARD_HISTORY first.

ParametersJSON Schema

Name	Required	Description
`mode`	Yes	DAILY_DIGEST: new tenders in last 24hrs. AWARD_HISTORY: past contract winners.
`limit`	No	Max results per source for AWARD_HISTORY (default 10)
`keyword`	No	Keyword for award history search. Required for AWARD_HISTORY.
`sources`	No	Sources to search. Defaults to all three.
`keywords`	No	Keywords to monitor or search (e.g. ["cybersecurity", "cloud infrastructure"]). Required for DAILY_DIGEST.

Output Schema

ParametersJSON Schema

Name	Required	Description
`mode`	Yes
`status`	No	Present on the free-tier preview path
`message`	No
`checked_at`	No
`_disclaimer`	No
`upgrade_url`	No

TDQS

A4.6/5.0

Behavior4/5

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

No annotations are provided, so the description carries full burden. It fully explains the two modes (AWARD_HISTORY and DAILY_DIGEST) and contextualizes their purpose. It also warns about the irreversibility of mispriced bids. However, it does not mention any rate limits, authentication requirements, or data freshness guarantees, which could be important for an agent.

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 well-structured with a clear lead sentence, followed by usage context, mode explanations, and a strong warning. It uses bullet points for the two modes, making it scannable. While it could be slightly more concise, every sentence contributes value, and it is front-loaded with the most critical information.

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?

Given that an output schema exists (not shown but mentioned), the description does not need to explain return values. It effectively covers all aspects: purpose, usage context, mode details, required parameters, and consequences. For a complex tool with two distinct modes, it provides complete context for an agent to make informed decisions.

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 significant meaning beyond the schema by explaining the purpose of each mode and the consequences of not using award history. It provides context that helps the agent choose parameters appropriately (e.g., 'AWARD_HISTORY: past contract winners for a keyword'). This elevates the score above baseline.

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 clearly states the tool retrieves tender intelligence including award history and daily digest. It uses specific verbs like 'retrieves' and identifies the resource (tender intelligence). It distinguishes from the sibling tool 'search_tenders' by focusing on competitive context for bid decisions rather than general tender search.

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?

The description provides explicit guidance on when to use the tool: 'Call this BEFORE your agent bids on any contract' and 'when a specific tender and needs competitive context'. It also gives a clear alternative: 'Submitting a bid without AWARD_HISTORY leaves your price uninformed...' and a direct warning: 'Do not bid without running AWARD_HISTORY first.'

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

search_tendersAInspect

Searches active government tenders across UK, EU, and US. Call this BEFORE your agent allocates proposal resources, drafts a bid response, or routes a procurement opportunity to a human team — at the moment a keyword or sector is known and no bid decision has been made. Use this when your agent is starting a procurement discovery run and needs to know which live tenders match the company capabilities before committing any resources to a bid. Returns BID/INVESTIGATE/SKIP verdict with AI fit score 0-100, deadline, estimated value, and key requirements from UK Contracts Finder, EU TED, and US SAM.gov simultaneously. A missed tender deadline cannot be recovered. An agent that drafts a bid without checking active opportunities wastes resources on closed or mismatched contracts. Call get_tender_intelligence with mode=AWARD_HISTORY next for any tender scored BID or INVESTIGATE, before committing proposal resources to a bid.

ParametersJSON Schema

Name	Required	Description
`limit`	No	Max results per source (default 10, max 25)
`keyword`	Yes	Search keyword — company capability, product type, or service (e.g. "cybersecurity", "catering", "IT support")
`sources`	No	Which sources to search. Defaults to all three: ["uk","eu","us"]
`days_old`	No	Only return tenders published in the last N days (default 30)
`min_score`	No	Only return tenders scoring above this threshold (default 50). Only applies when company_profile is provided.
`company_profile`	No	Description of the company capabilities and what contracts they are looking for. Used for AI fit scoring. More detail = better scores. If omitted, results are returned unscored.

Output Schema

ParametersJSON Schema

Name	Required	Description
`errors`	No
`keyword`	Yes
`scoring`	No	Present only when company_profile was provided
`tenders`	Yes
`checked_at`	Yes
`_disclaimer`	Yes
`total_found`	Yes
`_intelligence`	No	Always-present upsell hook -- not gated by tier
`sources_searched`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

Without annotations, the description carries full burden. It discloses the tool searches multiple sources simultaneously, returns a verdict with AI fit score and other fields, and warns about missed deadlines and wasted resources. It also provides workflow hints, ensuring transparency for an AI agent.

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 well-structured with the core purpose in the first sentence. It includes necessary usage guidelines and workflow hints, though it is slightly verbose with some redundancy. Overall, it earns its content.

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?

Given the presence of an output schema, the description need not detail return values, but it does mention key outputs (verdict, score, deadline, value, requirements). It also provides workflow context and consequences, making it complete for a search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with each parameter described. The description adds high-level workflow context but does not elaborate on individual parameters beyond what the schema provides. Baseline 3 is appropriate.

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 clearly states the tool searches active government tenders across UK, EU, and US. It uses specific verbs and resources, and distinguishes from the sibling tool 'get_tender_intelligence' by positioning it as the initial search step.

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?

The description explicitly states when to use this tool ('BEFORE your agent allocates proposal resources, drafts a bid response...') and provides context for its use in a procurement discovery workflow. It also advises calling 'get_tender_intelligence' next for scored tenders, offering clear guidance on alternatives.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

2 tool updatesv1.3.4Jul 29, 2026
- First observedget_tender_intelligence
- First observedsearch_tenders

Install Server

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TDQS

A4.5/5.0

Scored 2026-07-29 23:35 across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: search_tenders finds active tenders and provides an initial verdict, while get_tender_intelligence provides deeper competitive context on a specific tender. Their descriptions and usage guidance make disambiguation straightforward.

Naming Consistency5/5

Both tool names follow a consistent verb_noun pattern with underscores (search_tenders, get_tender_intelligence), using clear verbs and nouns that reflect their functionality.

Tool Count4/5

With only 2 tools, the server is minimal but focused on core procurement discovery tasks. While slightly below the typical 3-15 range, the narrow scope justifies the small count.

Completeness3/5

The server covers search and intelligence gathering, which are critical early steps, but lacks tools for acting on tenders (e.g., bidding, tracking). This leaves the workflow incomplete for end-to-end procurement support.

Maintenance

ActivityActive

ResponsivenessNo issues

Resources

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Tender MCP — Government Opportunity Intelligence for AI Agents

Quick Start

Harness Integration

Claude Code / Claude Desktop (.mcp.json)

LangChain (Python)

OpenAI Agents SDK (Python)

LangGraph

Why Use This

Tools

search_tenders

get_tender_detail

score_tender_fit (AI-powered — NOT a keyword match)

get_daily_digest (Paid only)

get_award_history (Paid only)

Recommended Workflow

Data Sources

Pricing

Reliability

Legal

Connect

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

govrider-mcp-server

Government Contracts MCP

EzBiz Government Contracting

oro-intel-mcpofficial

Latest Blog Posts

MCP directory API

`search_tenders`

`get_tender_detail`

`score_tender_fit` (AI-powered — NOT a keyword match)

`get_daily_digest` (Paid only)

`get_award_history` (Paid only)